Here in the U.S., this past summer saw an unprecedented number of emergency situations. Hurricanes blasted Texas, Florida, the U.S. Virgin Islands and Puerto Rico, leaving people stranded and without power, while wildfires ravaged the west.
So far this year, 15 separate weather and climate disasters have each caused at least $1 billion in damages in the U.S., according to the National Oceanic and Atmospheric Administration (NOAA), meaning, 2017 could tie 2011 for the most billion-dollar disasters. The USA Today chart shows those events.
In Oregon where I live, we experienced unprecendented smoky skies from wildfires — the hazardous air quality affected the health of many.
The silver lining? Growing expertise in the fields of disaster response, mapping, location awareness, UAVs and imagery. We continue to improve our ability to respond to disasters, such as with Waze traffic alerts for wildfire evacuations and UAVs that bring a virtual doctor to a crisis scene along with medicine. We use state-of-the-art technology to learn more about how, why and when disasters happen with tools such as UAVs that penetrate the mysteries of active hurricanes.
Orbital Witness will receive a voucher worth €50,000 for the acquisition of satellite data and will benefit from both technical and business coaching.
The competition encourages startups to innovate and develop new applications primarily based on Airbus’ satellite data. The winning British startup Orbital Witness proposes to use satellite imagery to provide a new perspective for legal due diligence in real estate.
Launched on May 30, the goal of the four-month challenge was to create added value for new businesses focusing on themes identified as important topics for the global population, ranging from forestry and agriculture to smart cities and maritime.
More than 130 projects from five continents were entered for the competition, among which 23 startups were pre-selected based on their originality and relevance as well as their technical and commercial feasibility.
These “semi-finalists” entered a subsequent round to further develop the proposals — this ended with a second selection phase in which the six finalists were chosen.
During the final, held Oct. 20 at the Airbus PlayLab in Toulouse, the six finalists presented their projects in front of representatives of different Airbus departments, including strategy, innovation, and marketing and sales.
The other finalists were:
23insights (the Netherlands), which tracks and predicts the human footprint in forests.
Ozius (Australia), which creates new landscape intelligence by fusing a variety of remote-sensing data to identify where the environmental risks and opportunities occurred in the past, where they are today, and project where they will occur in the future.
Ursa Space Systems Inc. (U.S.), which utilizes radar satellite data to deliver global and unbiased economic intelligence to energy and financial enterprises, providing reliable information about areas of the world that are traditionally opaque.
Qirate (Italy), which enhances position appeal for boosting business locations and helps people find their ideal place to live by rating the quality of life.
Kermap (France), which uses satellite imagery to support the ecological transition of cities.
The runner-up projects also received satellite data vouchers: €20,000 for 23insights, €15,000 for Ozius, €10, 000 for Ursa and €5,000 for Qirate and Kermap.
Commercial drone-maker Delair has introduced a professional unmanned aerial vehicle (UAV) for survey-grade photogrammetric mapping.
The UX11 is a small fixed-wing UAV that combines a powerful integrated onboard system, industry-grade sensors, limitless communication range and PPK centimeter-level positioning. It carries enough onboard computing power to access and process the pictures, then sends them to the operator in real-time.
According to the company, it will run automated quality checks on the images (such as blur detection or overlap checks) to help ensure the operator is acquiring quality data.
The UX11’s redundant communications system includes a proprietary line of sight radio and 3G/4G connectivity between the ground control station and the UAV using a worldwide machine-to-machine pre-paid plan.
Building on Delair’s experience with beyond visual line of sight (BVLOS) operations since 2012, the UX11 is ready for BVLOS flights with unlimited range and adds a new level of safety with this communication link.
The UX11 is lightweight, ultra-stable, simple to hand-launch at takeoff and it lands precisely where planned using distance measuring technology. New user-friendly Android mission planning software boasts innovative features such as support for in-flight camera feedback and live data review, the company said.
Made to help professionals in GIS, survey, and construction optimize area coverage per flight, the UX11 flies for 59 minutes with the best coverage and resolution specifications in its class for flights at 122 m (400 ft) altitude above ground level. The UX11 will be available for purchase via DELAIR’s global network of distributors by January, 2018.
The UX11 is a product offer for data acquisition which can be complemented by data processing and analytics software programs to address a range of commercial applications. Geospatial users can create 2D and 3D models and then generate elevation profiles, contour lines, slope qualifications and volumetric estimates with high accuracy and resolution using post-processed kinematic data and ground-control points.
Members of the public often turn to GPS World and Geospatial Solutions for help when their personal device gives them incorrect mapping information.
GPS.gov has set up a page that points users to the correct place to report problems, by walking them through a series of steps.
As our readers know, the problem isn’t with the satellites, but in the mapping software used by the devices and apps. Links are provided to mapping companies Google, Waze, TomTom, HERE, OpenStreetMap, Garmin and Apple.
When a platform’s mission requires maneuvering among different environments, transitions between these environments may mean that a single method cannot solve the full positioning, navigation and mapping problem.
This article describes an integrated navigation and mapping design using a GPS receiver, an inertial measurement unit, a monocular digital camera and three short-to-medium range laser scanners.
By Evan T. Dill and Steven D. Young, NASA Langley Research Center, and Maarten Uijt de Haag, Ohio University
An unmanned aircraft system (UAS) traffic management system (UTM) is an ecosystem for coordinating UAS operations in uncontrolled airspace, particularly operations under 400 feet altitude involving small- to mid-sized vehicles. In this domain, information services regarding the state of the airspace will be provided to UAS operators.
In addition, UTM would coordinate and authorize access to airspace for particular time periods based on requests from the operators. The Federal Aviation Administration would maintain regulatory and operational authority, and may for example, issue changes to constraints or airspace configurations to operators via this information service. However, there is no direct control from air traffic control personnel (such as “climb and maintain 300 feet” or “turn left, heading 150”).
As with visual flight rules operations of manned aircraft in uncontrolled airspace, under UTM the onus is on the vehicle operator to assure the flight system provides adequate performance with regard to communication, navigation and surveillance during flight. The vehicle/operator is responsible for avoiding other aircraft, terrain, obstacles and incompatible weather. UTM information services do not yet include, for example, information from an alternative positioning, navigation and timing system that may be needed for operations conducted in GPS-degraded environments (such as near buildings or other structures). This is the challenge being addressed by the integrated navigation concept described in this article. Other concepts are also being considered and developed for alternate, and unique, UAS missions and flight environments.
The method presented here employs a monocular camera as part of a multi-sensor solution that continuously operates throughout and between outdoor and structured indoor environments. For this work, an indoor environment is considered “structured” if its walls are vertical and remain approximately parallel, while the floor is either roughly flat or slanted.
In this type of environment, GPS is typically only sparsely available or not available at all. Hence, in our proposed navigation architecture, additional information from a camera and multiple laser range scanners (not the focus of this article) are used to increase the system’s positioning, navigation and mapping availability and accuracy in a GPS-challenged indoor environment. Figure 1 shows the target operational scenario, and Figure 2 the equipped multi-copter used in this research.
Figure 1. Operational scenario: open-sky environment, transition to indoor and indoor environment.Figure 2. Hexacopter sensors and sensor locations.
Figure 3 shows a block diagram for the methodology implemented in this research, with the elements related to monocular camera methods highlighted. When assessing the capabilities of each of the sensors used in the work, only the inertial sensor produces data that is solely dependent on the motion of the platform and local gravity and is more or less unaffected by its surroundings. Therefore, the inertial is chosen to be the primary sensor for this method.
The mechanization integrates the measurements from GPS, the laser scanners and the monocular camera through a complementary Kalman Filter (CKF) that estimates the errors in the inertial measurements and feeds them back to the inertial strapdown calculations. For this inertial error estimation method to function properly, pre-processing methods must be implemented that relate the sensors’ observables to the inertial measurements.
Here we describe the processing techniques necessary to relate measurements from a monocular camera to measurements from the inertial measurement unit (IMU). Then we show how these techniques are used in the broader GPS/optical/inertial mechanization and present testing results.
Figure 3. Monocular camera components of a broader mechanization.
2D Monocular Camera Methods
To process data from the camera, we first perform feature detection and tracking of both point features and line features. Specifically, elements from Lowe’s Scale Invariant Feature Transforms (SIFT) are used to track point features, which are in turn used to obtain estimates of the camera’s rotational and un-scaled translational motion using structure from motion (SFM) based methods. To resolve the ambiguous scale factor, a novel scale estimation technique is employed that uses data from the platform’s horizontally scanning laser. This technique as well as algorithms that produce a 3D visual odometry solution are presented below.
SIFT Point Feature Extraction. To aid in determining camera motion, SIFT has been used as a way of identifying local features that are invariant to translation, rotation, and image scaling. This technique yields 2D point features that are unique to their surroundings and readily identified and associated across a set of sequential camera images. Each key location and its surroundings are analyzed, resulting in a descriptive 128-element feature vector, known as a SIFT key. Example results of the SIFT key identification process are shown in Figure 4.
Figure 4. SIFT feature identification.
Based on the results of the SIFT feature extraction process from two image frames, a feature association function is performed using the feature vectors. For this work, a two-step procedure is implemented.
First, SIFT keys are associated using a matching procedure. Example results of this process are shown in Figure 5, where it can be observed that incorrectly associated features may result from this process. To remove these artifacts, inertial measurements are utilized to ensure the correctness of the associations.
Figure 5. SIFT matching results between consecutive image frames.
Using a triangulation method, prospective associations are used to crudely estimate each feature’s 3D position with respect to the previous frame. While this triangulation method yields 3D data, it is of poor quality, and is therefore only used to obtain rough approximations that are sufficient for association purposes, but insufficient for navigation purposes.
Once transformed to a 3D reference frame, the projected distances of each feature are compared with one another, and prospective associations that produce significantly different depths than surrounding points are eliminated. Example results of this filtering process can be seen in Figure 6.
Figure 6. Point feature association after inertial based miss-association rejection.
In future implementations, the ORB feature will be evaluated, as its performance is expected to be more than two orders of magnitude faster than SIFT.
Wavelet Line Feature Extraction. To implement the scale factor estimation technique described in a later section, it is necessary to first extract and track vertical line features. To accomplish this, a method using wavelet transforms (WTs) was developed. When applied to a 2D image, WTs can be viewed as filters operating in the x and y directions of an image. By applying either a high- or low-pass filter to both of an image’s channels (that is, x and y directions), four sub-images are formed to represent an image approximation. For this work, a level-one bi-orthogonal 1.3 wavelet was used to decompose each image. An example of the four sub-images produced by this wavelet is shown in Figure 7 along with the original image.
Figure 7. Example results of wavelet decomposition.
Through further processing of the vertical decomposition results, strong line features are identified by first inspecting the illuminated elements along the vertical channels of the decomposed image and identifying clusters of adjacent pixels. Next, a 2D line fit is applied to the groups to estimate residual noise. Pixel collections with low residuals (<3 here) are considered valid line features. Example results of this process are shown in Figure 8.
Figure 8. Example vertical line extraction results.
For association purposes, lines cannot be compared over a sequence of image frames solely based on location as similar line features may not necessarily possess the same endpoint, and, therefore, can be of varying lengths. However, corresponding lines will possess many common points and similar orientations if they are projected into the same frame. Using the inertial reference frame, each line’s orientation, , can be transformed across image frames as given by:
In this manner, lines between frames that contain multiple similar points and have comparable orientations are considered associated.
For a discussion of the projective visual odometry and epipolar geometry methodology as well as the resolution of true metric scale used in this work, download the supplemental PDF.
Metric Scale. As the unscaled translation estimate calculated through the aforementioned visual odometry method is a unit vector, it only indicates the most likely direction of motion of the camera. To obtain the sensor’s actual translational motion, an estimate of the scale factor, m, is required to determine the absolute translation ∆r. This can be accomplished through techniques using a priori knowledge of the operational environment or measurements from other sensors. In this research effort, a new method is employed that makes use of data provided by a horizontally scanning laser.
The proposed method estimates the scale in an image by identifying points in the environment that are simultaneously observed by the camera and the forward-looking laser range scanner.
To enable this estimation method we must identify the correspondences between the pixels in the camera images (each defined by a direction unit vector corresponding to the row x and column y) and the laser scanner measurements (each defined by direction unit vector). A calibration procedure establishes these correspondences. Given the laser range measurements, 2D features located on the scan/pixel intersections can be scaled up to 3D points.
Unfortunately, extracted 2D point features are rarely illuminated by a laser scan in two consecutive frames. This can be resolved by considering the intersection of a laser scan with 2D line features rather than point features. As the laser intersects the camera frame at the same location regardless of platform motion, and the platform does not make excessive roll and pitch maneuvers, vertical line features in the image frame are preferred as they will be relatively orthogonal to the laser scan plane.
Using the previously described vertical line extraction procedure, Figure 9 shows an example image frame overlaid with the points in the image frame illuminated by the laser (indicated by a blue line) and the extracted vertical line features (indicated as green lines). Multiple intersections of 2D vertical lines with laser scan data are calculated (indicated as red points). Inversely, Figure 10 depicts the location of all laser scan points in green, all laser points observable with the camera field-of-view (FoV) in blue, and intersection points in red.
Figure 9. Image frame overlaid with points; Laser (blue), vertical line features (green), multiple intersections (red).Figure 10. 2D vertical line and laser intersections in laser scan data.
For scale factor calculation purposes, it is necessary to track the motion of these 3D laser/vision intersection points, across sequences of camera image frames. As each intersection point uniquely belongs to a line feature in the 2D image frame, it can be stated that if two lines are associated, their corresponding intersection points are also associated. Using the rotation computed from the visual odometry process, the line association method described by (1) is implemented, and provides associations between laser/vision intersection points across frames.
To calculate the desired scale factor based on these associated laser/vision points, geometric relationships are established: unit vectors from the camera center to points located on a 2D line. From these, the line’s normal vector can be derived.
Monocular Camera Results
To assess the performance of the visual odometry processes, multiple experiments were conducted. The results of one such test are discussed here. During each test, the visual odometry results for rotation, shown in blue, were easily evaluated through comparison with the platform’s inertially-measured rotation, displayed in red.
The rotational results for each sensor were decomposed into the Euler angles: pitch, roll and yaw with respect to an established navigation frame. Unfortunately, the inertial sensor itself cannot be used to evaluate the visual odometry translation results due to relatively large inertial drift in the sensor measurements. As no independent measurements were available to evaluate translation with high precision, the truth reference was established by accurately measuring the actual paths taken during each flight.
A test flight was conducted traversing a rectangular indoor hallway loop. This test contained translation in multiple dimensions, large heading changes and a flight duration of four minutes. Moreover, this test allowed for evaluation of the eight-point algorithm and scale estimation method in the presence of rapid scene changes.
The attitude estimation results for this test are shown in Figure 11. Throughout data collection, the maximum separation between the inertial and vision-based attitude estimators for pitch, roll and yaw was 9°,19° and 14°, respectively. Upon comparison to many of the other conducted tests, the maximum attitude errors were larger. There are multiple reasons for this increase. First, the duration of this experiment was greater than that of previous experiments. Errors accumulate as a function as time due to integration of residual bias errors, so increasing flight duration will increase cumulative error.
Next, the looping path observed throughout this test caused the eight-point algorithm and scale estimation procedures to quickly adapt to differing scenery. Drastic scene changes (turning a corner) increase the difficulty of feature association between frames. This directly affects the procedures used for visual odometry in an adverse manner. Finally, there are situations in this flight where features are sparse. In general, a decrease in features will cause a decrease in the estimation capabilities of visual odometry.
Figure 12 shows the visual odometry path calculated for experiment 2. Here, the estimated length of each of the four straight legs of the rectangular loop matches to within 2 meters of the measured hallway lengths. This implies that the scale estimation technique is working reasonably well.
Figure 12. Visual odometry path determination while traveling around an indoor loop.
As for the estimated translational directionality produced by the eight-point algorithm, the first two legs of the loop never divert from the measured path by more than 2 meters; the third leg diverts by 5 meters. This is most likely due to a lack of well dispersed features in that specific hallway.
The cumulative error contained in the third linear leg of the loop also makes evaluation of the final leg difficult. However, if previous errors are removed, the final leg appears to match the measured path well. In total, the landing position calculated through visual odometry is 6.5 meters away from the measured end of the trial.
Integration Methodology
In cases where GPS measurements are available along with the visual odometry solution, the proposed method can extend the GPS/IMU integration mechanization. The structure of the referenced GPS/inertial integration consists of two filters: a dynamics filter that uses GPS carrier-phase measurements to estimate velocity and other IMU errors, and a position filter that uses the velocity output of the dynamics filter and GPS pseudoranges. The dynamics filter can be adapted and extended to include camera data within its mechanization.
The dynamics filter is a CKF designed to estimate the inertial error states: velocity error in the north-east-down (NED) coordinate reference frame, misorientation (including tilt error), gyro bias error, and specific force or accelerometer bias error. This yields a state vector. For a discussion of the state vector, download the supplemental PDF.
Results
To evaluate the proposed algorithms, data was collected through multiple flights of the hexacopter platform shown in Figure 2 through a structured indoor and outdoor environment including transitions between these two environments. The availability of GPS measurements in these environments ranged from fully denied, to substantially degraded, to enough observables for a full solution.
The results of one test flight are discussed in this section. Apart from the data collections with the hexacopter, truth reference maps were created for the indoor operational environment and used for evaluation of the described processes. The results of the full GPS/inertial/laser/camera integrated solution described in Figure 3 are shown in an NED frame in Figure 13.
Figure 13. Path compared to 2D reference map.
The truth reference of the environment, depicted in red (derived from a terrestrial laser scanner), is compared to the flight path obtained from the extended Kalman filter (EKF), displayed in blue. The estimated flight trajectory constantly remains within the hallway truth model, indicating sub-meter level performance. Furthermore, based on an extension of this work for environmental laser mapping produced from the EKF, combined with the accuracy of the map, it is further reinforced that sub-meter-level navigation performance is obtained.
During portions of the described data collection, there was enough visibility (>3 satellites) to calculate a GPS position. The availability of GPS measurements to the position estimation portion of the filter allowed for geo-referencing of the produced flight path and 3D map.
Figure 14 displays the geo-referenced continued flight path based on the integration filter superimposed on Google Earth on the left, while the standalone GPS solution based on pseudoranges only is plotted on the right. The geo-referenced path correctly displays the platform passing through Stocker Center, the Ohio University engineering building.
To demonstrate the contributions of the monocular camera to the above results, laser measurements were removed from the solution for a 20-second period where GPS was unavailable. During the 20-second removal of laser data, the system is forced to operate on integration between visual odometry measurements and the IMU. The cumulative effect caused by this situation can be observed in Figure 15. After coasting on an IMU/camera solution for 20 seconds, the path is subsequently altered by 3 meters, as opposed to the solution with all sensors.
Figure 15. Effect of losing GPS and lasers for 20 seconds.
To further emphasize the contribution of the visual odometry component, both the laser and camera were removed from the integration for the same 20-second period. During this time frame the EKF is forced to coast on calibrated inertial measurements. The effect of losing all secondary sensors for a 20-second period can be observed in Figure 16.
Figure 16. Effect of coasting on the IMU for 20 seconds.
During the forced sensor outage, a 45-meter cumulative difference is introduced between the path using all sensors and the path with denied sensors. Through comparison of the results shown in Figure 15 and Figure 16, the contribution of monocular camera data can be isolated.
When the EKF was forced to operate for 20 seconds using an IMU/camera solution, 3 meters of error were introduced. This is significantly smaller than the 45 meters of error observed when using only the inertial for the same period. Thus, the camera is shown to provide stability to the EKF when neither the laser nor GPS are available.
Conclusions
The visual odometry techniques produced reasonably good attitude estimation and are effective at constraining inertial drift when other sensors are not available. The inclusion of camera measurements to the discussed integrated solution resulted in increases in the accuracy, availability, continuity and reliability of the system.
Acknowledgment
The material in this article was first presented at the ION Pacific PNT conference in Hawaii, May 2017.
Manufacturers
The camera used aboard the UAV in these tests is a Point Grey Firefly MV and the IMU is an XSENS MTi. The GPS receiver is a NovAtel OEMStar with a corresponding NovAtel L1 patch antenna.
EVAN T. DILL is a research scientist in the Safety Critical Avionics Systems Branch at NASA Langley Research Center. He received his Ph.D. in electrical engineering from Ohio University.
STEVEN D. YOUNG is a senior research scientist at NASA with more than 30 years of experience in the related fields of safety assurance, avionics systems engineering and human-machine interaction.
MAARTEN UJIT DE HAAG is the Edmund K. Cheng Professor of Electrical Engineering and Computer Science and a Principal Investigator (PI) with the Avionics Engineering Center at Ohio University, where he earlier earned his Ph.D. in electrical engineering.
Hexagon AB showcased its geospatial and construction portfolio at Intergeo 2017 in Berlin, Germany.
Hexagon’s sensor portfolio combined with a range of software creates solutions that support the geospatial and construction industries.
According to the company, visitors were able to explore a number of solutions, including mobile mapping; asset collection and management for geographic information systems (GIS); 3D laser scanning; photogrammetry; remote sensing; airborne sensors and unmanned aerial vehicles (UAVs); global positioning and monitoring like GNSS; construction project controls and progress documentation; utility detection; measurement software and cloud-based dynamic mapping. Hexagon will be at booth A1.024 in Hall 1.1.
“Hexagon is focused on creating smart digital realities,” said Hexagon President and CEO Ola Rollén. “At Intergeo, we will demonstrate the productivity and savings that can be realized from digitalizing customer workflows, automating processes and ensuring all stakeholders have access to dynamic, critical information.”
During the conference portion of Intergeo, Hexagon executives addressed the growing need for digitalization in geospatial and construction industries:
Transformation through digitalization. Hexagon Geosystems President Juergen Dold provides the Intergeo opening keynote exploring the need for businesses to transform from efficient digitisation to connected digitalisation for continued progress.
The power of combining cost, schedules and models in the cloud. Director of Global Business Development for HxGN SMART Build at Hexagon PPM, Cathi Hayes, explains how SMART Build integrates model, schedule, cost and digital layout capabilities into a single solution that addresses the most critical phases of construction planning and execution.
Hexagon integrated solution for utility detection and mapping. Leica Geosystems Construction Tools President Katherine Broder and IDS GeoRadar President Alberto Bicci present how to achieve high productivity in mapping utilities with Hexagon’s underground detection portfolio, including ground penetrating radar (GPR) solutions.
Escaping the flatlands. Hexagon Geospatial President Mladen Stojic envisions new and easier approaches that ingest the influx of data, use automated approaches to extract the signal from the noise and provide intuitive ways of communicating insights to decision makers and field teams so they can shape smarter change.
Connecting perceptions with reality in the world of BIM, GIS and survey. Leica Geosystems Laser Scanning Vice-President of Business Development Faheem Khan looks at the benefits of sensor fusion, the growth of digital reality solutions and how both are affecting project lifecycles in the real, digital world.
Streamlining UAV workflows for surveying, construction and inspection. Leica Geosystems Product Manager for UAV Solutions Valentin Fuchs and Leica Geosystems Director of Marketing and Communications for UAV Solutions Benjamin Federmann deliver a series of presentations and hands-on demonstrations on how Hexagon integrates UAVs as part of the technology tool kit to digitalise workflows.
At Intergeo 2017, Hemisphere GNSS released its next-generation multi-frequency, multi-GNSS S321+ and C321+ GNSS smart antennas.
The S321+ and C321+ are upgrades to the previous versions S321 and C321 and offer added benefits, according to the company. Powered by the Eclipse P326 OEM board, the smart antennas support 394 channels and can simultaneously track all satellite signals including GPS, GLONASS, BeiDou, Galileo and QZSS, making them robust and reliable.
S321+ and C321+ come standard with two long-life lithium batteries providing up to 12 hours of operation. The batteries are hot-swappable, so users can change them without stopping work, maximizing efficiency and return on investment.
The S321+ and C321+ GNSS smart antennas are being featured at the Hemisphere GNSS booth in Hall 2.1/stand C2.008 at Intergeo 2017 in Berlin, Germany, Sept. 26-28.
The S321+ and C321+ combine Hemisphere’s Athena GNSS engine and Atlas L-band correction technologies with a new webUI, offering an unparalleled level of customer-friendly performance. The ruggedized antennas are designed for challenging environments; both meet IP67-standard requirements.
The S321+ and C321+ come in two versions, with 4G LTE optimized for either North American or international locations.
Powered by Athena GNSS engine, the S321+ and C321+ provide best-in-class, centimeter-level RTK. Athena excels in virtually every environment where high-accuracy GNSS receivers can be used. Tested and proven, Athena’s performs with long baselines in open-sky environments under heavy canopy, and in geographic locations experiencing significant scintillation.
“The S321+ and C321+ represent the advanced technology, durability, and ease of use that our customers have come to expect,” said Miles Ware, director of marketing at Hemisphere GNSS. “By upgrading these systems with increased functionality and management capabilities, we are offering unbeatable value to the industry.”
Atlas GNSS Global Corrections. The S321+ and C321+ ship pre-configured to test-drive corrections from Hemisphere’s Atlas L-band correction service. The bundled solution provides users worldwide with an easy way to utilize Atlas, including Hemisphere’s Atlas H10 service offering 8 cm 95 percent accuracy (4 cm RMS). They also use Hemisphere’s aRTK technology, powered by Atlas, which allows the receivers to operate with RTK accuracies when RTK corrections fail. If the S321+ and C321+ are Atlas-subscribed, they will continue to operate at the subscribed service level until RTK is restored.
The S321+ is designed for use in applications such as land or marine survey, GIS, mapping, and construction. Together with SureFix, Hemisphere’s advanced processor, the S321+ delivers high-fidelity RTK quality information that results in guaranteed precision with virtually 100% reliability.
The C321+ was designed specifically for construction environments, adding another system component that empowers heavy equipment manufacturers to deliver their own machine control and guidance solutions to their customers. The C321+ can also be paired with Hemisphere’s recently announced SiteMetrix site management software platform that helps manage all of your construction jobsite activities, including grade and volume checking.
The NeoRTK System is a high-performing GNSS RTK system. It includes a multi-constellation and multi-frequency GNSS engine and various communication protocols. With a high-end GNSS antenna inside, NeoRTK can speed the time to first fix (TTFF) and improve the capability of anti-jamming. The 16G internal storage and up to 32G external SD card, along with the built-in large-capacity battery for 10-hour field work, improves surveyors’ productivity, while the radio module makes long distance operation more convenient. A smart personal digital assistant offers high readability and fast access to essential functions and modes. The NeoRTK system also has an adjustable measurement rod with automatic tilt compensation.
Real-time professional-grade positioning information
The SXblue Platinum is a high-accuracy GNSS receiver compatible with iOS, Windows and Android Bluetooth. Powered by 394 channels, the SXblue Platinum uses all constellations (GPS, GLONASS, Galileo, BeiDou and QZSS) with triple frequency, and provides the ability to use global or local coverage for corrections (SBAS, L-band and RTK). With the scalable SXblue Platinum Basic, users can activate any frequency or constellation at any time following initial purchase. The receiver is also field-upgradable, which means that these options can be remotely activated when convenient. It also has an L-band signal correction via Hemisphere’s Atlas service. With its new Tracer technology, the receiver can sustain its level of accuracy when the Atlas signal is interrupted.
Turns Android devices into data-collection systems
Trimble Catalyst DA1 antenna attaches to a smartphone running a Catalyst-enabled app.
The Catalyst software-defined GNSS receiver for Android devices is now available through Trimble’s global distribution network. Through Catalyst and a special antenna, customers can access positioning-as-a-service to collect geolocation data with Trimble or third-party apps on smartphones, tablets and mobile handhelds. When combined with a plug-and-play digital antenna and subscription to the Catalyst service, the receiver provides on-demand GNSS positioning capabilities to turn consumer Android devices into centimeter-accurate data-collection systems. Catalyst requires a Catalyst-enabled location app for Android; a Catalyst subscription, with accuracy options ranging from 1 meter to centimeter level, and the small, lightweight DA1 antenna, which plugs directly into Android smartphones and tablets. A range of Catalyst-enabled applications have been developed for geographic information system (GIS) data acquisition, cadastral land management, topographic mapping and ground control for unmanned aircraft systems (UAVs).
Updated with improved workflows and innovative features
ArcGIS Pro 2.0 is Esri’s next-generation desktop geographic information system (GIS). It is tightly integrated with the rest of the ArcGIS platform, so that users can complete more of their workflows solely in ArcGIS Pro, such as map creation and data management. Getting started with new projects has vastly improved with Favorites. In ArcGIS Pro, users can modify topology properties directly. An enhanced traverse tool improves COGO workflows. Highly requested context menu options for importing and exporting data included in the Catalog pane. 3D navigation controls enable exploration of 3D landscapes, and views of 3D and 2D maps can be synced. Layouts are more useful and powerful with embeddable dynamic interactive charts. Improvements to 3D drawing including feature drawing by camera distance. Enhanced lighting of 3D objects make 3D visualizations even better. Analytics improvements include fill-missing-values tools and enhanced spacetime cubes.
For pilots to use GPS as primary means of navigation
The GPS-4000S sensor provides GPS-based navigation and enables GPS-based approaches for aircraft equipped with flight management systems. The sensor’s Space-Based Augmentation System (SBAS) capabilities enable use of GPS as the primary means of navigation in areas of SBAS coverage. The GPS-4000S uses up to 10 GPS satellites and two geostationary SBAS satellites. However, users can calculate navigation with a minimum of four GPS satellites with acceptable geometry or three satellites plus calibrated barometric altitude. With additional satellites, the system’s Receiver Autonomous Integrity Monitoring (RAIM) detects and isolates defective satellites while improving navigation accuracy. Predictive RAIM capability determines if the future satellite geometry at the destination airport will support planned arrival procedures.
Efficient image recognition engine and functional safety
Renasas autonomy is an advanced driving assistance system (ADAS) and automated driving platform. The first rollout under the new platform is the R-Car V3M high-performance image recognition system-on-chip (SoC), optimized for use in smart camera applications, surround view systems and lidars. For smart camera applications, the R-Car V3M focuses on enabling NCAP (New Car Assessment Program) features. It is equipped with an integrated ISP and delivers high performance for computer vision, while supporting low power consumption and a high level of functional safety. The R-Car V3M SoC complies with the ISO26262 safety standard, delivers low-power hardware acceleration for vision processing and is equipped with a built-in image signal processor, freeing up board space and reducing system manufacturers’ costs.
GTN 650 is a fully integrated solution in a small package ready and approved for installation in hundreds of makes and models of aircraft, including helicopters, by the U.S. FAA, Europe EASA, Canada TCCA and Brazil ANAC. It combines GPS, communication and navigation functions with powerful multifunction display capabilities such as high-resolution terrain mapping, graphical flight planning, advanced navigation, multiple weather options, connectivity and traffic display. The SBAS/WAAS-certified, 15-channel GPS receiver generates five position updates per second, letting pilots fly GPS-guided localizer performance with vertical guidance (LPV) glidepath instrument approaches down to as low as 200 feet. The system includes a complete package of very high frequency (VHF) navigation capabilities, with a 200-channel VHF omni-directional radio range (VOR)/instrument landing system (ILS) with localizer and glideslope.
Device includes critical driving and business tools
The OverDryve 7 Pro is part of Rand McNally’s OverDryve OS Connected Vehicle platform. It is E-Log ready and has a high-resolution 7-inch screen. Designed for truck drivers, the OverDryve 7 Pro has truck-specific navigation and routing with points of interest, advanced lane guidance, toll costs, warnings and fuel logs. Other features include hands-free calling and texting, voice assistance and in-cab entertainment. The powered magnetic mount includes a commercial-grade GPS boost. The unit comes pre-loaded with the Rand McNally DriverConnect2 logbook app, which can be paired with a compatible Rand McNally electronic logging device (ELD) to provide a fully compliant electronic logging solution.
The Here+ RTK GNSS kit, is built around the u‑blox NEO‑M8P high-precision real-time kinematic (RTK) GNSS module. HEX offers an open-source drone autopilot, the Ardupilot, which the kit supports. The kit consists of a round rover designed to be mounted on the drone. It is connected to the flight controller using the supplied 8‑pin CLIK-Mate connector (for the autopilot Pixhawk2) or an optional 4 pin + 6 pin DF13 connector (for the Pixhawk1). The base station with its smaller GNSS receiver and an external antenna complete the equipment. HEX’s goal is to promote open source drone technology to a larger community and assist drone companies with affordable accessories for a wealth of applications, such as agricultural drone, powerline inspection, precision farming, logistics or 3D mapping.
Hybrid electric propulsion provides longer operating time
The Hercules is a long-endurance multi-rotor UAS with a hybrid electric propulsion system and patent-pending aerodynamic design improvements. These two technologies enable the aircraft to fly up to 3.5 hours or carry a 4-pound payload for 2 hours. The aircraft has a 36-pound gross weight and is intended for FAA Part 107 operations. Hercules is useful for applications that benefit from long endurance such as precision agriculture, mapping, first responders and infrastructure inspection. The increased flight time enables up to 45% reduction in cost per acre for the operator to acquire data, while the increased payload capacity avoids repeat overflights with swapped out payloads.Three gallons of fuel is enough energy to fly the aircraft for the whole day. The battery contains enough energy to fly the aircraft for an additional 2 minutes following failure of the combustion engine, enabling the aircraft to make a safe landing.
Survey-level accuracy for small unmanned aerial systems
The Loki GNSS positioning system allows users of DJI Phantom 4 Pros and Inspire 2 drones, as well as most drones using higher end cameras, to achieve survey-level accuracy with minimum ground control. For positioning accuracy, Loki uses the Septentrio AsteRx-m2 GNSS engine with 448 hardware channels. A patent-pending method by GeoCue detects camera events from the UAV and synchronizes them to GNSS positioning. Loki is a self-contained kit that provides the hardware and software needed to equip a drone with a post-processed kinematic (PPK) multi-frequency, multi-constellation, differential, carrier-phase GNSS. The adapter cable is splug and play. Using a local base station (not included), Loki provides centimeter-level positioning with minimal, and in some cases, no ground-control points (though GCPs are always recommended for quality assurance).
The Phoenix Ranger RL1-UAV produces photorealistic 3D point-cloud data collected efficiently over extensive regions. For law enforcement, the data can provide greater context, awareness and tactical accuracy. Agencies typically use ground-based lidar as a forensic crime-scene mapping technology. Aerial lidar is efficient for larger, outdoor scenes because line-of-sight issues can restrict ground lidar scans from capturing the entire area. Benefits for law enforcement include exposing unmapped trails hidden in remote backwoods; determining width, elevation and length of roads; detecting micro topography hidden by vegetation; and gathering ground-surface information affected by human activities. The Phoenix Ranger RL1-UAV provides survey-grade (cm-level) accuracy with 920-meter laser range and outstanding intensity calibration. Options include IMU and dual-GPS upgrade for increased accuracy.
Harxon is showcasing a series of GNSS antennas and wireless data-link modems at 2017 Intergeo, being held Sept. 26-28 in Berlin, Germany.
The products aim to provide the user better industrial solutions in the fields of surveying and mapping, precision agriculture and unmanned aerial vehicles (UAVs).
The Harxon D-Helix Antenna.
D-Helix Antenna: The multi constellation antenna is capable of superior tracking signals from 4 satellite constellations, including GPS L1/L2 L-Band, GLONASS L1/L2, BDS B1/B2/B3 and Galileo. The innovative quadrifilar helix antenna design of low wind-resistance is ideal for aerial photographs, telemetry technology, disaster monitoring and security monitoring industries. Its 3.5dBi peak gain ensures exceptional low elevation tracking performance. The low noise figure enhanced transmission interference reduction and improve the signal quality.
The Harxon GPS 1000 Survey Antenna.
Survey Antenna GPS 1000: The all constellation GNSS antenna has passed the NGS certification, which receives GPS L1/L2/L5 L-Band, BDS B1/B2/B3, GLONASS L1/L2, Galileo E1/E2/E5a/E5b signals. It can be used in land survey, marine survey, channel survey and agriculture applications, with a consistent performance across the full bandwidth. GPS 1000 has high gain and wide beam width to ensure the signal receiving performance of satellite at the low elevation angle, and the phase center remains constant as the azimuth and elevation angle of the satellites change. The influence of measurement error can be minimized via the multi-feed design and embedded multi-path rejection board.
Rover Radio HX-DU1603D: The high-speed, Bluetooth-enabled ruggedized UHF rover radio is designed for GNSS/RTK surveying and positioning. It ensures the data communication between 410MHz and 470 MHz in either 12.5KHz or 25 KHz channels. HX-DU1603D is equipped with a Bluetooth transceiver for wireless communications of external devices, features a 6800mAh rechargeable internal battery and configurable transmit power between 0.5W and 2W, also the IP67 waterproof capability allows outdoor long operational hours.
Harxon Frequency Hopping Module HX-DU1018D/HX-DU2017D.
Frequency Hopping Module HX-DU1018D/HX-DU2017D: The built-in frequency hopping transceiver modules are small size, light weight, low power consumption and strong resistance to disturbance. They provide a reliable, high speed and low latency data transmission, which are suitable for UAV flight control. These modules support a band range among 400MHz, 840MHz and 900MHz and long distance of communication. Besides, HX-DU1018D/HX-DU2017D can realize a switchover between air baud rate and serial port baud rate.
Harxon Smart Antenna.
Smart Antenna: It is a multi-functional GNSS product which is integrated by multi-frequency OEM antenna, OEM receiver and frequency hopping transceiver. Smart Antenna utilizes the dual anti-multipath antenna to receive stable GNSS signals under the bad-signal environment and precisely output the direct information with a centimeter-level positioning accuracy. The IP67 waterproof design allows the smart antenna for a long time outdoor operation.
The Harxon H-RTK.
H-RTK: H-RTK is for UAV positioning and navigation, which reaches the positioning accuracy to a centimeter level. It is integrated with positioning, height setting and heading functions to provide accurate, reliable solutions. H-RTK ensures the positioning accuracy to a centimeter level for a more stable flightpath. Also, it provides the reliable height information and solve the height-error problem to prevent air turbulence. H-RTK outputs precise navigation information with powerful magnetic disturbance resistance, it enables the flight reliability under a magnetic disturbance environment, and avoid security risks. The built-in anti-interference frequency hopping transceiver helps data transfer back to the base station, and supports the frequencies of 400 MHz, 840 MHz and 900 MHz.
For more information,visit Harxon’s booth at Intergeo in Hall 4.1 booth C4.013.
In Portland, Oregon, and in Berlin, Germany, the two largest and most important international conferences on GPS, GNSS, PNT, survey, mapping and geodesy take place this year on exactly the same dates — just 5,177 miles apart. Now that’s bad timing. Our strategy is to divide our forces and send key personnel to interact with industry leaders at each gathering — to bring you the news and developing stories you need to keep on the forefront of change.
If you’re at ION GNSS+ or Intergeo, look for these faces, come up and introduce yourselves. We want to talk with you! If you’re not fortunate enough to attend either conference, look to our website, newsletters and this magazine for product launches, videos and in-depth stories filed from the developing frontiers of PNT. We’ll be reporting !!Live!! and for weeks, even months, to come.
Attending Intergeo in Berlin:
Burch
Barwacz
Joyce
Gerard
Tim Burch is our survey editor; in his day job he’s a professional surveyor and board of directors secretary of that profession’s national society.
Allison Barwacz is digital media content producer for North Coast Media (NCM, that’s us) with a passion for videography and writing.
Mike Joyce and Ryan Gerard, senior account manager and account manager, respectively, work closely with our marketing partners, who make this magazine and multi-media communications channel possible.
Attending ION GNSS+ in Portland:
Stoltman
Whitford
Mitchell
Cozzens
Harms
Sabau
Limpert
Cameron
Langley
Kevin Stoltman is founder and president of NCM, with a distinguished career in business-to-business publishing.
Marty Whitford is editorial director and publisher; earlier, he actually worked at GPS World and attended ION-GNSS 2004.
Michelle Mitchell is account manager for GPS World and senior marketing and event manager for NCM. She knows the GPS industry landscape and players extremely well.
Tracy Cozzens is our managing editor, with her hands on all the controls.
Joelle Harms is an award-winning digital media manager, focused on content planning and creation.
Joe Sabau is an account manager with a keen eye for market trends.
Kelly Limpert is a digital media content producer developing a strong online and social media presence for all of our partners.
Richard Langley is GPS World’s innovation editor and a professor at the University of New Brunswick.
To assist with Hurricane Harvey and Irma emergency response and damage assessments efforts, Remote GeoSystems is donating LineVision software licenses to official agency, volunteer and non-profit drone operators.
In addition to supporting a Texas A&M team responding to Harvey, LineVision is being pre-deployed to volunteers organized by Florida State University’s Emergency Management and Homeland Security Program to help with the Hurricane Irma search and rescue and damage assessment.
Any other volunteer teams, first responders and non-profit organizations providing essential response and recovery services are encouraged to complete the contact form to request free copies of LineVision software for disaster relief efforts.
LineVision lets emergency response teams easily map drone video of Hurricane Harvey damage assessments. (Image: Remote Geosystems)
The LineVision solution is a commercial software suite for UAV, airborne and terrestrial mobile inspection and survey projects requiring geo-referenced video playback, analysis, collaboration and reporting using standard Esri maps and data, Esri ArcMap and Google Earth GIS applications.
Using the software, anyone with a GPS-enabled video camera, drone or geospatial DVR that can geotag video in the proper format can immediately load their videos and photos to Esri ArcGIS and Google Earth along with compatible geospatial data.
As the video plays, a position marker moves along an aerial or terrestrial GPS track positioned on a map, continuously indicating where the current frames were recorded. Users may also geospatially “navigate” a video recording by simply clicking a single point along an aerial or terrestrial GPS track.
The video then automatically advances to that point in the recording so that users can visually interpret what was recorded at that specific place and time. If something of interest is detected in the video, users may also “snap” an image from the video, which is geotagged and saved for future analysis.
In addition to video, users can import photos and documents from disaster survey and assessment projects. All these imported data types can be saved in a Remote GeoSystems “geoProject” file for data portability, reporting and future analysis in other versions of LineVision desktop, cloud and server applications.
Help with Harvey
Remote GeoSystems was contacted by the Texas A&M Engineering Experiment Station Center for Robot-Assisted Search and Rescue (CRASAR), who was deployed with the Fort Bend County Office of Emergency Management.
All parties involved moved quickly, and within a few hours after being contacted, drone video data collection teams were using various versions of the company’s donated LineVision video and photo mapping software to map and view interactive UAV flight tracks with corresponding videos in Esri ArcGIS and Google Earth GIS software.
The software is being used to help visualize, distribute and share the data available from a record 119 UAS flights that CRASAR conducted over 11 days, including 61 flights on a single day.
“We first learned about Remote GeoSystems’ LineVision software for mapping geotagged video from drones about a year ago, and at that time even did a proof of concept demo for the USCG and first responders,” said Justin Adams, Air Operations Branch Director for Fort Bend County Manned/Unmanned Ops and CRASAR director of operations for Harvey. “Now with the Texas Gulf Coast facing a long and difficult assessment and recovery process and Hurricane Irma bearing down on Florida, it became clear now was the time to deploy this valuable UAV solution to operators and volunteers working the affected areas.
“I have been involved in manned and unmanned aviation for the better part of two decades and Remote Geo offers not only the simplest, but most complete solution for rapid geospatial aerial and ground-based disaster assessment and reporting in the industry.”
Key Features of LineVision
Play videos from single and multi-camera data collection platforms
“Click-on-Map” video navigation
Set a custom geo-fence around the moving position marker
Load Esri ArcGIS or Google Earth-compatible geospatial data files
Save video and photo work as geoProjects for simple project reporting, archive and search
Esri released a new book, “The ArcGIS Book: 10 Big Ideas about Applying The Science of Where,” as well as a companion website.
According to Esri, the book provides mapmakers with the know-how and hands-on experience to practice “The Science of Where.” In addition, the accompanying website offers information and interactive education resources needed to use web-based geographic information system (GIS) technology to create maps, work with apps, create and use authoritative data, conduct spatial analysis and more.
The book is available in print, as an interactive PDF and online. It explains how to use Esri’s ArcGIS platform to manage and analyze data and then visualize and share that information in maps to gain location-based insight, the company said. The book’s chapters cover web mapping, ready-to-use apps, story maps, 3-D GIS, spatial analysis, imagery and the Internet of Things, as well as curated content from Esri’s Living Atlas of the World.
In addition, the online and PDF versions of The ArcGIS Book are interactive with 10 Learn ArcGIS lessons and links to 250 online maps and apps from Esri and the worldwide ArcGIS community. According to Esri, it also includes a variety of electronic learning resources, including software downloads, videos, case studies, story maps, e-books, open data sites, the Living Atlas of the World and more.
“It’s a multimedia experience,” said Christian Harder, the writer at Esri who co-edited the book with Clint Brown, Esri director of product engineering. “Every graphic and image in the electronic versions of the book comes to life in the interactive versions. This makes it an excellent starting point for people to learn about GIS or communicate to their friends and colleagues what GIS is all about.”
