DJI has launched a new Local Data Mode that stops internet traffic to and from its DJI Pilot app, providing enhanced data privacy assurances for sensitive government and enterprise customers.
Local Data Mode will be available in the next update on the DJI Pilot app on CrystalSky and for select Android tablets.
When an operator activates Local Data Mode, the app will stop sending or receiving any data over the internet. This adds an additional layer of security for operators of flights involving critical infrastructure, governmental projects or other sensitive missions.
“We are creating Local Data Mode to address the needs of our enterprise customers, including public and private organizations that are using DJI technology to perform sensitive operations around the world,” said Brendan Schulman, DJI’s vice p resident of policy and legal affairs. “DJI is committed to protecting the privacy of its customers’ photos, videos and flight logs. Local Data Mode will provide added assurances for customers with heightened data security needs.”
Since Local Data Mode blocks all internet data, the DJI Pilot app will not be able to detect the location of the user or show map and geofencing information such as No Fly Zones and temporary flight restrictions, nor will it notify drone operators of firmware updates.
Telemetry data on flight logs such as altitude, distance or speed will remain stored on the aircraft even if the user deactivates Local Data Mode.
Whether Local Data Mode is activated or not, photos and videos captured by the user are always stored on the drone’s SD card and are only shared if the user chooses to upload them online to the SkyPixel community, social media or other websites.
When using Local Data Mode, drone operators are reminded that they are solely responsible for the safety of their flight operation and that they understand that features that may enhance and support the safety of their operations, but that rely on internet connectivity, are no longer available.
Drone operators can enable Local Data Mode by opening the DJI Pilot app, clicking on “Activate LDM Mode” and entering a password which will be required to deactivate Local Data Mode when they decide to go online again.
New drones will still have to be activated first by logging into the user’s DJI account with an email and a password. To ensure the drone has the latest firmware, users can download and update it while they have internet connectivity before re-activating Local Data Mode.
The Local Data Mode feature may not be available in locations where an internet connection is required or highly advisable due to local regulations.
Measure, a U.S. provider of drone services to enterprise customers, has added turnkey wind farm inspection capabilities to its portfolio of aerial data collection solutions.
Wind farm operators can outsource preventive maintenance inspections to Measure’s drone pilots and data analysts for fast, accurate, safe and timely problem identification. The service helps avert critical turbine failures and efficiency losses while reducing repair downtime and its associated revenue impact.
The company’s drone inspection solution has already been used to successfully examine more than 400 MW of wind farms. The package spans all inspection and reporting functions, including state-of-the-art drone equipment, safe and insured flights by experienced drone pilots, efficient data processing that pinpoints both blade damage and severity, and damage reports and analytics available through a secure online portal.
Dry Lake Wind Power Project, Arizona (Photo: U.S. DOE)
Measure’s new wind farm inspection solution expands the company’s services to the renewable energy sector, which also include a robust suite of drone inspection solutions for solar plants that was announced in July.
The suite includes solar-panel inspections, drone-based site overview and maintenance, site shading and terrain analysis, thermal inverter scans, tracker misalignment detection and vegetation management analysis.
Benefits of Measure’s drone-based blade and tower inspections include:
75% faster inspections than other methods, averaging 30 minutes or less per turbine compared to as much as two hours for manned inspections. This reduces excessive time commitments and allows large wind farms to be inspected more frequently. It also reduces labor costs for inspection and frees employees for other tasks.
Decreased injury risk in the field, with no threat of falls to inspectors climbing turbine structures or blades.
Better defect and damage detection because drones get closer to turbine blades than ground cameras, capturing clearer images. Undetected defects on the blades can result in continuous efficiency losses as high as 6% and associated revenue loss of up to $10,000 annually per turbine.
Maximized turbine availability and revenue generation through early problem detection that helps prevent critical failures and associated downtime for repairs.
Actionable data, including classified damage reports and historical portfolio analysis documenting turbine defects, failure rates and efficiency losses over time. Damage reports can be customized to display only the information needed by blade repair technicians with a few clicks.
“Many wind farms don’t inspect their turbines on a preventive maintenance basis, and those that do use ground crews with conventional cameras and zoom lenses. Under both conditions, there is a risk of failing to detect turbine damage or structural defects on blades that can worsen over time and lead to a catastrophic failure,” said Harjeet Johal, Measure vice president of energy infrastructure and a 10-year veteran of the renewable energy industry with a Ph.D. in electrical engineering. “Our drone-based inspections provide multiple advantages that can help wind farm operators operate at peak capacity.”
“Our global wind portfolio is currently 1,033 MW with 877 MW in the U.S. alone. Knowing the health of our wind assets is essential for us to provide reliable power to our customers,” said Adam Brown, U.S. Drone Program Lead at The AES Corporation, a Fortune 200 global power company. “Using drones to inspect the blades and towers makes it safer for our people as they can stay firmly on the ground while still being able to inspect, at scale, hundreds of wind turbines to ensure they have the highest availability.”
Hurricane Harvey is the first major catastrophe in which drones have been used on a large scale by both government and commercial operators, said Ken Long, an analyst at the Freedonia Group.
UAVs are also likely to find widespread use if Hurricane Irma either directly strikes or skirts the east coast of Florida early next week, as current projections show.
In addition to helping keep emergency workers safe by allowing them to look for people trapped by floodwaters and inspect damage in high-risk areas, drone use can speed up the recovery process. Drones can be flown over structures such as fuel tanks, power lines and railroad tracks before they can be reached by land, enabling government agencies and utilities to identify what is in most urgent need of repair.
They also allow insurance adjusters to more quickly process claims, enabling rebuilding efforts to get underway faster. Farmers Insurance reports that an insurance inspector using a drone can complete up to eight times the number of home inspections each day than he or she otherwise would be able to do.
When Hurricane Harvey first made landfall in Texas on Aug. 25, the Federal Aviation Administration (FAA) set up a temporary but extensive no-fly zone over Houston and nearby areas to help protect first responders in helicopters and other manned aircraft. This flight ban included all drone operations except those specifically approved by the FAA.
https://youtu.be/XRdUV4WqnDE
In the 10 days that followed Hurricane Harvey, the FAA issued more than 100 separate authorizations for drone use in the Houston area, according to the Wall Street Journal. Some of the applications for drone use were reviewed and approved by the FAA within hours, an unusually fast turnaround time for an agency that typically takes days or weeks to make decisions.
With the exception of a handful of flights conducted by media firms, all of the approved operations were for drones used in conjunction with, or on behalf of, government agencies. Drones were used to inspect bridges, roadways and power lines; assess the condition of oil refineries and water plants; and survey coastal damage.
As the flood waters continued to recede and flight restrictions were eased or lifted, insurance companies — including Allstate, Farmers Insurance, Travelers and USAA — began to use drones to assess property damage and speed claims processing.
However, drone use by insurance companies and other commercial users is limited by FAA rules that do not allow them to be flown above 400 feet, outside the visual line of sight of the operator, or above people not directly involved in their operation, unless a waiver is granted.
These regulations could change with a 2018 FAA reauthorization bill being considered by Congress.
“The demonstrated usefulness of drones in Hurricane Harvey response and recovery efforts could well influence the content of that legislation,” Long said.
Even if the current FAA regulations remain in place, U.S. commercial drone demand will expand rapidly from what is currently an extremely small market base, according to the Freedonia Group’s Drones (UAVs) study. “Non-military government use of drones will also climb at a robust rate through 2020,” Long said.
Both commercial and non-military government market gains will be fueled by further improvements in drone designs, making them more capable and easier to operate, customized for use in specific applications and cost-saving.
Honeywell has launched its first commercial unmanned aerial vehicle (UAV) inspection service — the Honeywell InView inspection service — to help industrial customers improve critical structure inspections while helping increase employees’ safety from many of the risks associated with these often-dangerous working conditions.
Intel Falcon 8+ octocopter drone.
The Honeywell InView inspection service will combine the proven performance of the Intel Falcon 8+ UAV system and Honeywell’s expertise in the aerospace and industrial industries with data-driven software customized to the needs of the utility, energy, infrastructure, and oil and gas industries, the company said.
The Honeywell InView inspection service package, which includes the components of the UAV, pilot app and customizable web portal, helps customers organize and create standards around their routine and crisis-response inspections.
For example, the Honeywell InView inspection service can help utility customers create routine inspections of transmission and distribution systems that generate data that can be stored, searched and accessed from in the office and out in the field on demand.
“This collaboration combines Intel’s advanced commercial Intel Falcon 8+ UAV system with Honeywell’s leadership in aerospace safety and connectivity to deliver solutions that deliver reliable, efficient and actionable information to utility and industrial customers,” said Carl Esposito, president, Electronic Solutions, Honeywell Aerospace. “Through our extensive industrial experience, our customers will also gain access to Honeywell’s customized software and data solutions that will help them log, analyze, and eventually predict or prevent outages and structural failures, while protecting the men and women called upon to complete these crucial but high-risk jobs.”
“We are incredibly pleased to collaborate with Honeywell on this exciting new business opportunity,” said Anil Nanduri, general manager for Intel’s UAV business group. “The safety, flight precision and robust performance of the Intel Falcon 8+ system are a perfect fit for the Honeywell InView inspection service and will allow its customers to inspect, collect and analyze valuable data in a whole new way.”
With Honeywell’s InView inspection service, customers tap into Honeywell’s experience across vertical segments such as utilities, aerospace, connected building management, and oil and gas technologies.
In collaboration with Intel, Honeywell will utilize the intelligence and experiences of its diverse set of businesses to give customers a comprehensive solution and experience unrivaled in the marketplace.
“Technology, along with the Internet of Things, is enabling utilities around the world to modernize the management of their energy grids,” said Nitin S. Kulkarni, president, Smart Energy, Honeywell Home and Building Technologies. ” Honeywell brings together the technology that allows utilities to transform how energy is consumed in homes and buildings with software-based systems that help safely and efficiently manage complex industrial facilities and utility grids. Honeywell also has more than 100 years of experience providing dependable products and services to a variety of industries, of which Honeywell InView inspection service is the latest entry.”
Inspection Service goals
Keeping workers safe. According to the U.S. Department of Labor, utility line workers have one of the top 10 most dangerous jobs in the United States, with 21.5 annual fatalities from high-voltage lines for every 100,000 workers.
By using the inspection service, utility companies can send a UAV to perform routine inspections of substations, transmission towers and power lines while keeping boots on the ground and workers safe.
For utilities, using a UAV for inspections offers safer and more cost-effective means than existing methods using helicopters, cherry pickers, ladders and walking inspections.
Improving efficiency. Historically, inspections are siloed by organization and by individuals within organizations. Honeywell’s InView inspection service aims to create standardized inspections where customers can create operational efficiencies in the office and out in the field.
Data capture and analysis. UAVs are being touted for their data-gathering capabilities, but without analytics, more data is simply more data. Honeywell’s service can synthesize vast quantities of data to identify only what is needed and actionable, translating workers’ tacit knowledge into valuable information that provides actionable insights for business.
Connected Freight
Honeywell and Intel also recently collaborated to create a Connected Freight platform that gives shippers and logistics companies the unprecedented ability to monitor shipments of high value and perishable goods, helping prevent costly damage and loss.
The new Honeywell InView inspection service continues the work these two companies are doing to help various industries use connected devices to be more efficient and safer, and harness data in new and meaningful ways.
A manufacturer of refinery infrastructure was about to finish the assembly of a radiant box when a thumbnail-size notch was noticed in one of the pipes just before it was to be installed. The radiant box facility is used in the process of refining hydrogen under very high temperature (1,300 to 2,000°F) and pressure (45 to 360 psi).
The Elios by Flyability is a collision tolerant drone.
The notch was noticed near the end of the assembly process of the 144 40-foot-high vertical pipes composing the radiant box. The refinery owner insisted that each of the installed pipes be inspected thoroughly before moving to the final stages of testing and firing up the radiant box.
The refinery manufacturer faced a difficult problem. Made of a particular heat-resistant alloy containing 30 percent chrome, the pipes need careful treatment — contact with another alloy could damage them, which made use of scaffolding impractical. Instead, the customer turned to Industrial SkyWorks and its indoor inspection drone, Elios by Flyability.
The complexity of the location, the large number of pipes, and the fact that they could easily be mixed up required a meticulous work approach by Industrial SkyWorks. The two-man UAV crew set up a charging station just outside the building. Four flights were needed per pipe to ensure complete coverage. Using the onboard lights of the Elios, the UAV flew to the top of each pipe and descended slowly, recording video.
The Elios drone flew continuously for nearly five days in a dry and dusty environment, imaging both sides of each pipe. Once finished, the crew presented high-resolution video of each pipe to the satisfied client.
Resulting savings are estimated at 75 percent for cost and 85 percent for time, the company said. For instance, using a UAV avoided the need for workers to work at height with the associated safety procedures.
NASA’s UTM. On May 25, the Federal Aviation Administration (FAA)-designated Nevada UAS Test Site and its NASA partners flew five different unmanned aerial vehicles (UAVs) to test NASA’s Unmanned Aircraft System Traffic Management (UTM).
The flights demonstrated multiple operational scenarios, including parachute-initiated emergency supply deliveries and aerial survey operations.
The UAVs were flown beyond the pilot’s visual line of sight (BVLOS) using strategically placed visual observers and sophisticated command and control, communication and detect-and-avoid technologies.
The test is part of a three-week national campaign, which NASA is leading in close collaboration with the FAA and industry partners on a more complex version of its UTM technologies at six different UAS Test Sites around the nation.
Demonstration in France. In France, Delair-Tech flew a UAV for 30 miles, simulating powerline inspection. Delair used a regular, commercial 3G cellphone network to control the drone for the test — an innovative demonstration that long-distance drone operations can be safe and simple to achieve.
Canadian Deliveries.Drone Delivery Canada Corp. (DDC) hit a pivotal milestone toward commercializing its drone logistics platform after achieving BVLOS in test flights. Systems tested include DDC’s FLYTE management system, avoidance technology and communications platform.
During flights in Alberta, DDC’s Mission Control Centre in Toronto, 2,500 kilometers away, successfully monitored and record telemetry in real time. DDC could become the first drone logistics-compliant operator approved by Transport Canada.
Contributing Editor Tony Murfin is on vacation this month. In place of his column, we bring you an advance look at an important UAV show as applied to surveying and mapping, and a story about drone use in surveillance.
In the zone
Legal issues, international market analyses and best practices will take center stage at the Interaerial Solutions Expo (IASEXPO), which will take place Sept. 26–28 in conjunction with Intergeo 2017 in Berlin, Germany.
At IASEXPO, the international UAV sector will be demonstrating the potential for civil and commercial UAV applications. IASEXPO will consist of an exhibition, forum and the FlightZone for UAV demonstrations. About 150 providers from 25 countries are expected to represent the young drone market at the IASEXPO.
IASEXPO’s practical forum will cover the latest topics with renowned experts. Visitors don’t have to walk far to switch between market overviews and expert presentations. The aim is to efficiently combine the trade fair and talks.
IASEXPO Forum 2016.
Regulations. As Germany’s drone regulations come into force this year, the legal aspects of using and operating UAVs is a key focus of the practical forum. Multicopters and drones weighing more than two kilograms can now only be flown in Germany by someone who holds a “drone driving license.” Pilots will be able to take the drone license test at the trade fair.
Frank Wichert from project management company procow will detail the requirements and reveal the precise procedure that pilots must follow. Speaker Ulrich Dieckert is a lawyer and expert on the approval process; he specializes in exceptions to operating bans that hinder drone work.
Market prospects. Kay Wackwitz, CEO of Drone Industry Insights, will present economic analyses of application opportunities and limits for UAVs, and discuss market developments and collaborations.
UAV Issue Manager Ralf Heidger from German traffic control (DFS) will discuss how DFS tackles the challenge of drones in the air space and tracking them within the air-traffic-management system.
Best practices. First-hand reports will provid examples of best practices in using drones for surveying and inspecting buildings and industrial complexes. Friedrich Wilhelm Bauer from Hannover University of Applied Sciences and Arts will highlight use of thermal-imaging technology for inspections. Benjamin Federmann from Aibotix-Leica will discuss the economic benefits of using drones in surveying and construction.
The German Association of Copter Pilots will weigh the question of whether to “make or buy” needed drones and services. Answers come from success stories in niche segments such as 3D modeling and smart framing. Maik Neuser from Westnetz and Carlo Zgraggen from Aeroscout will discuss inspections in the energy sector.
Other topics will be the use of drones in agriculture, forestry and disaster relief. Antoine Cottin from Carbomap and Bobby Vick from Precisionmapper will speak to the practical forum on drones used for surveying forests.
Drones on patrol
UAVs will soon be a common sight over border zones, crime hotspots and city streets in South Africa, as public safety and security officials and police departments discover the cost saving and efficiencies offered by drone patrol “armies,” according to Airborne Drones, a South African-based manufacturer of enterprise-grade drones.
Airborne Drones Vanguard 35-km long range surveillance drone ready to take flight. (PRNewsfoto/Airborne Drones)
Drones provide a solution to the limitations of other surveillance methods such as GPS tracking, CCTV camera observation, biometric surveillance and ground patrols. Aerial surveillance is increasingly being harnessed for security monitoring — traditionally, with costly helicopters. Drone surveillance present an faster and cheaper method of data collection.
Specialized security drones can enter narrow and confined spaces, produce minimal noise, and can be equipped with night-vision cameras and thermal sensors, allowing them to provide imagery that the human eye is unable to detect. In addition, UAVs can quickly cover large and difficult-to-reach areas, reducing staff numbers and costs, and don’t require much space for operators.
Autonomous, long-range security drones are at the vanguard of new policing methods, accoring to Airborne Drones. “Offering live video feeds to ground control stations, these drones can range autonomously over pre-programmed flight paths for extended periods of time, allowing for ongoing routine patrols across wide areas such as borders, maritime regions and high security installations.
Should an incident be detected, ground crews can then follow objects or intruders from a safe distance, providing visual support to safety and security teams. UAVs can provide detailed visual documentation of sites, enabling effective analysis, risk management and security planning.”
Around the world. Numerous countries are rolling out security drones to support public safety and defense initiatives”, says Airborne Drones. Israel has long harnessed advanced drones for military surveillance, and recently sold a fleet of “spy drones” to the Irish army.
The U.S. FBI has used drones for surveillance and tracking for several years. In Australia, the new $50 million Defence Cooperative Research Centre will develop long-range drones, automated vehicles and robots to help Australian soldiers fight the wars of the future. India is looking to military-grade UAVs for maritime and other surveillance and intelligence gathering.
In June, Brazil’s São Paulo became the first Latin American city to use drones for public security surveillance, and in July, Hamburg, Germany, deployed surveillance drones for the estimated 100,000 demonstrators at the G20 summit. In Australia’s New South Wales, the authorities are using helicopter and drone surveillance along the coast to protect holiday-goers from rip currents and sharks.
UAVs are also instrumental in managing transport infrastructure safety and security and event security, from event security infrastructure to spectator and crowd control and safety, to overall health and safety planning.
Kongsberg Geospatial’s IRIS UAS situational awareness application now provides a certifiable option to monitor drones and airspace. Kongsberg Geospatial is an Ottawa-based developer of real-time geospatial visualization software.
The IRIS UAS Airspace Situational Awareness application meets the requirements of the DO-278A Assurance standard for air traffic management systems.
By anticipating the regulatory requirements for airspace visualization with Unmanned Traffic Management or UTM, the IRIS display will be a regulatory approved component increasing the safety of commercial drone flight operations — especially when operating beyond visual line-of-sight (BVLOS).
IRIS UAS program director Paige Cutland uses the IRIS UAS airspace situational awareness application to monitor the progress of a drone on a beyond line-of-sight (BVLOS) mission from a portable ground control station set up in a trailer.
Kongsberg Geospatial has been providing software design assurance to meet the certification requirements for real-time geospatial and spatial awareness technology to support air traffic management, air defense applications and unmanned systems for nearly three decades.
Their IRIS UAS situational awareness application had its genesis in supporting military UAV flight operations and was developed to help operators safely pilot UAVs in BVLOS operations. It was also used by regional airspace UTM managers to monitor the operations of multiple drones simultaneously.
The DO-278A standard (Guidelines for Communication, Navigation, Surveillance and Air Traffic Management [CNS/ATM] Systems Software Integrity Assurance) is the primary standard used by certification authorities such as FAA, EASA and Transport Canada to provide the assurance of software contained in non-airborne CNS/ATM systems. Unmanned systems manufacturers that build ground control stations for commercial drone systems, and airports and port authorities that create airspace control systems are anticipated to have to meet this standard when designing and building new systems.
By developing an airspace awareness application that satisfies this standard, Kongsberg Geospatial has provided a key component for unmanned systems manufacturers, airport operators and port authorities that wish to develop ground-based monitoring systems that are safe and certifiable for commercial operations.
“Unmanned Traffic Management and safe airspace operations will require certification of technology,” said Ranald McGillis, president of Kongsberg Geospatial. “We believe providing a certifiable airspace application will dramatically increase the safety of unmanned flight operations wherever it’s in use.”
OVER THE YEARS, many philosophers tried to describe the phenomenon of inertia but it was Newton, in his Philosophiæ Naturalis Principia Mathematica, who unified the states of rest and movement in his First Law of Motion. One rendering of this law states: Every body continues in its state of rest, or of uniform motion in a straight line, unless it is compelled to change that state by forces impressed upon it. Newton didn’t actually use the word inertia in describing the phenomenon, but that is how we now refer to it.
In his other two laws of motion, Newton describes how a force (including that of gravity) can accelerate a body. And as we all know, acceleration is the rate of change of velocity, and velocity is the rate of change of position. So, if the acceleration vector of a body can be precisely measured, then a double integration of it can provide an estimate of the body’s position. That sounds quite straightforward, but the devil is in the details. Not only do we have to worry about the constants of integration (or the initial conditions of velocity and position), but also the direction of the acceleration vector and its orthogonal components. Nevertheless, the first attempts at mechanizing the equations of motion to produce what we call an inertial measurement unit or IMU were made before and during World War II to guide rockets.
Nowadays, IMUs typically consist of three orthogonal accelerometers and three orthogonal rate-gyroscopes to provide the position and orientation of the body to which it is attached. And ever since the first units were developed, scientists and engineers have worked to miniaturize them. We now have micro-electro-mechanical systems (or MEMS) versions of them so small that they can be housed in small packages with dimensions of a few centimeters or embedded in other devices.
One problem with IMUs, and with the less-costly MEMS IMUs in particular, is that they have biases that grow with time. One way to limit these biases is to periodically use another technique, such as GNSS, to ameliorate their effects. But what if GNSS is unavailable? Well, in this month’s column we take a look at an ingenious technique that makes use of how the human body works to develop an accurate pedestrian navigation system — one whose accuracy has been checked using drone imagery. As they might say in New York, “Hey, I’m walking (with accuracy) here!”
Satellite navigation systems have achieved great success in personal positioning applications.
Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem.
One such solution is a personal pedestrian dead-reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians.
Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment — problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength.
In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors.
PDR DESIGN AND POSITIONING METHOD
Our plan in designing a pedestrian positioning system was to use a high-rate IMU device strapped onto the pedestrian’s shoe together with an EKF-based framework. The main idea of this project was to use filtering algorithms to estimate the errors (biases) accumulated by the IMU sensors. The EKF is updated with velocity and angular rate measurements by zero-velocity updates (ZUPTs) and zero-angular-rate updates (ZARUs) separately detected when the pedestrian’s foot is on the ground. Then, the sensor biases are compensated with the estimated errors.
Therefore, the frequent use of ZUPT and ZARU measurements consistently bounds many of the errors and, as a result, even relatively low-cost sensors can provide useful navigation performance. The PDR framework, developed in a Matlab environment, consists of five algorithms:
Initial alignment that calculates the initial attitude with the static data of accelerometers and magnetometers during the first few minutes.
IMU mechanization algorithm to compute the navigation parameters (position, velocity and attitude).
Pace detection algorithm to determine when the foot is on the ground; that is, when the velocity and angular rates of the IMU are zero.
ZUPT and ZARU, which feed the EKF with the measured errors when pacing is detected.
EFK estimation of the errors, providing feedback to the IMU mechanization algorithm.
INITIAL ALIGNMENT OF IMU SENSOR
The initial alignment of an IMU sensor is accomplished in two steps: leveling and gyroscope compassing. Leveling refers to getting the roll and pitch using the acceleration, and gyroscope compassing refers to obtaining heading using the angular rate.
However, the bias and noise of gyroscopes are larger than the value of the Earth’s rotation rate for the micro-electro-mechanical system (MEMS) IMU, so the heading has a significant error. In our work, the initial alignment of the MEMS IMU is completed using the static data of accelerometers and magnetometers during the first few minutes, and a method for heading was developed using the magnetometers.
PACE-DETECTION PROCESS
When a person walks, the movement of a foot-mounted IMU can be divided into two phases. The first one is the swing phase, which means the IMU is on the move. The second one is the stance phase, which means the IMU is on the ground. The angular and linear velocity of the foot-mounted IMU must be very close to zero in the stance phase. Therefore, the angular and linear velocity of the IMU can be nulled and provided to the EKF. This is the main idea of the ZUPT and ZARU method.
There are a few algorithms in the literature for step detection based on acceleration and angular rate. In our work, we use a multi-condition algorithm to complete the pace detection by using the outputs of accelerometers and gyroscopes.
As the acceleration of gravity, the magnitude of the acceleration ( |αk| ) for epoch k must be between two thresholds. If
(1)
then, condition 1 is
(2)
with units of meters per second squared. The acceleration variance must also be above a given threshold. With
(3)
where is a mean acceleration value at time k, and s is the size of the averaging window (typically, s = 15 epochs), the variance is computed by:
. (4)
The second condition, based on the standard deviation of the acceleration, is computed by:
. (5)
The magnitude of the angular rate ( ) given by:
(6)
must be below a given threshold:
. (7)
The three logical conditions must be satisfied at the same time, which means logical ANDs are used to combine the conditions:
C = C1 & C2 & C3. (8)
The final logical result is obtained using a median filter with a neighboring window of 11 samples. A logical 1 denotes the stance phase, which means the instrumented-foot is on the ground.
EXPERIMENTAL RESULTS
The presented method for PDR navigation was tested in both indoor and outdoor environments. For the outdoor experiment (the indoor test is not reported here), three separate tests of normal, fast and slow walking speeds with the IMU attached to a person’s foot (see FIGURE 1) were conducted on the roof of the Institute of Space Science and Technology building at Nanchang University (see FIGURE 2). The IMU was configured to output data at a sampling rate of 100 Hz for each test.
For experimental purposes, the user interface was prepared in a Matlab environment. After collection, the data was processed according to our developed indoor pedestrian dead-reckoning system. The processing steps were as follows: Setting the sampling rate to 100 Hz; setting initial alignment time to 120 seconds; downloading the IMU data and importing the collected data at the same time; selecting the error compensation mode (ZARU + ZUPT as the measured value of the EKF); downloading the actual path with a real measured trajectory with which to compare the results (in the indoor-environment case).
For comparison of the IMU results in an outdoor environment, a professional drone was used (see FIGURE 3) to take a vertical image of the test area (see FIGURE 4). Precise raster rectification of the image was carried out using Softline’s C-GEO v.8 geodetic software. This operation is usually done by loading a raster-image file and entering a minimum of two control points (for a Helmert transformation) or a minimum of three control points (for an affine transformation) on the raster image for which object space coordinates are known. These points are entered into a table. After specifying a point number, appropriate coordinates are fetched from the working set. Next, the points in the raster image corresponding to the entered control points are indicated with a mouse.
FIGURE 3. Professional drone. (Photo: DJI)
For our test, we measured four ground points using a GNSS receiver (marked in black in Figure 4), to be easily recognized on the raster image (when zoomed in). A pre-existing base station on the roof was also used. To compute precise static GPS/GLONASS/BeiDou positions of the four ground points, we used post-processing software. During the GNSS measurements, 16 satellites were visible. After post-processing of the GNSS data, the estimated horizontal standard deviation for all points did not exceed 0.01 meters. The results were transformed to the UTM (zone 50) grid system. For raster rectification, we used the four measured terrain points as control points. After the Helmert transformation process, the final coordinate fitting error was close to 0.02 meters.
For comparing the results of the three different walking-speed experiments, IMU stepping points (floor lamps) were chosen as predetermined route points with known UTM coordinates, which were obtained after raster image rectification in the geodetic software (marked in red in Figure 4).
After synchronization of the IMU (with ZUPT and ZARU) and precise image rectification, positions were determined and are plotted in Figure 4. The trajectory reference distance was 15.1 meters.
PDR positioning results of the slow-walking test with ZARU and ZUPT corrections were compared to the rectified raster-image coordinates. The coordinate differences are presented in FIGURE 5 and TABLE 1.
FIGURE 5. Differences in the coordinates between the IMU slow-walking positioning results and the rectified raster-image results. (Chart: Authors)
Table 1. Summary of coordinate differences between the IMU slow-walking positioning results and the rectified raster-image results. (Data: Authors)
The last two parts of the experiment were carried out to test normal and fast walking speeds. The comparisons of the IMU positioning results to the “true” positions extracted from the calibrated raster image are presented in FIGURES 6 and 7 and TABLES 2 and 3.
FIGURE 6. Differences in the coordinates between the IMU normal-walking positioning results and the rectified raster-image results. (Chart: Authors)FIGURE 7. Differences in the coordinates between the IMU fast-walking positioning results and the rectified raster-image results. (Chart: Authors)Table 2. Summary of coordinate differences between the IMU normal-walking positioning results and the rectified raster-image results. (Data: Authors)Table 3. Summary of coordinate differences between the IMU fast-walking positioning results and the rectified raster-image results. (Data: Authors)
From the presented results, we can observe that the processed data of the 100-Hz IMU device provides a decimeter-level of accuracy for all cases. The best results were achieved with a normal walking speed, where the positioning error did not exceed 0.16 meters (standard deviation). It appears that the sampling rate of 100 Hz makes the system more responsive to the authenticity of the gait.
However, we are aware that the test trajectory was short, and that, due to the inherent drift errors of accelerometers and gyroscopes, the velocity and positions obtained by these sensors may be reliable only for a short period of time. To solve this problem, we are considering additional IMU position updating methods, especially for indoor environments.
CONCLUSIONS
We have presented results of our inertial-based pedestrian navigation system (or PDR) using an IMU sensor strapped onto a person’s foot. An EKF was applied and updated with velocity and angular rate measurements from ZUPT and ZARU solutions.
After comparing the ZUPT and ZARU combined final results to the coordinates obtained after raster-image rectification using a four-control-point Helmert transformation, the PDR positioning results showed that the accuracy error of normal walking did not exceed 0.16 meters (at the one-standard-deviation level). In the case of fast and slow walking, the errors did not exceed 0.20 meters and 0.32 meters (both at the one-standard-deviation level), respectively (see Table 4 for combined results).
Table 4. Summary of coordinate differences between the IMU slow-, normal- and fast-walking positioning results and the rectified raster-image results. (Data: Authors)
The three sets of experimental results showed that the proposed ZUPT and ZARU combination is suitable for pace detection; this approach helps to calculate precise position and distance traveled, and estimate accumulated sensor error.
It is evident that the inherent drift errors of accelerometers and gyroscopes, and the velocity and position obtained by these sensors, may only be reliable for a short period of time. To solve this problem, we are considering additional IMU position-updating methods, especially in indoor environments. Our work is now focused on obtaining absolute positioning updates with other methods, such as ZigBee, radio-frequency identification, Wi-Fi and image-based systems.
ACKNOWLEDGMENTS
The work reported in this article was supported by the National Key Technologies R&D Program and the National Natural Science Foundation of China. Thanks to NovAtel for providing the latest test version of its post-processing software for the purposes of this experiment. Special thanks also to students from the Navigation Group of the Institute of Space Science and Technology at Nanchang University and to Yuhao Wang for his support of drone surveying.
MANUFACTURERS
The high-rate IMU used in our work was an Xsense MTi miniature MEMS-based Attitude Heading Reference System. We also used NovAtel’s Waypoint GrafNav v. 8.60 post-processing software and a DJI Phantom 3 drone.
MARCIN URADZIŃSKI received his Ph.D. from the Faculty of Geodesy, Geospatial and Civil Engineering of the University of Warmia and Mazury (UWM), Olsztyn, Poland, with emphasis on satellite positioning and navigation. He is an assistant professor at UWM and presently is a visiting professor at Nanchang University, China. His interests include satellite positioning, multi-sensor integrated navigation and indoor radio navigation systems.
HANG GUO received his Ph.D. in geomatics and geodesy from Wuhan University, China, with emphasis on navigation. He is a professor of the Academy of Space Technology at Nanchang University. His interests include indoor positioning, multi-sensor integrated navigation systems and GNSS meteorology. As the corresponding author for this article, he may be reached at [email protected].
CLIFFORD MUGNIER received his B.A. in geography and mathematics from Northwestern State University, Natchitoches, Louisiana, in 1967. He is a fellow of the American Society for Photogrammetry and Remote Sensing and is past national director of the Photogrammetric Applications Division. He is the chief of geodesy in the Department of Civil and Environmental Engineering at Louisiana State University, Baton Rouge. His research is primarily on the geodesy of subsidence in Louisiana and the grids and datums of the world.
FURTHER READING
• Authors’ Work on Indoor Pedestrian Navigation
“Indoor Positioning Based on Foot-mounted IMU” by H. Guo, M. Uradziński, H. Yin and M. Yu in Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 63, No. 3, Sept. 2015, pp. 629–634, doi: 10.1515/bpasts-2015-0074.
“Usefulness of Nonlinear Interpolation and Particle Filter in Zigbee Indoor Positioning” by X. Zhang, H. Guo, H. Wu and M. Uradziński in Geodesy and Cartography, Vol. 63, No. 2, 2014, pp. 219–233, doi: 10.2478/geocart-2014-0016.
• IMU Pedestrian Navigation
“Pedestrian Tracking Using Inertial Sensors” by R. Feliz Alonso, E. Zalama Casanova and J.G. Gómez Garcia-Bermejo in Journal of Physical Agents, Vol. 3, No. 1, Jan. 2009, pp. 35–43, doi: 10.14198/JoPha.2009.3.1.05.
“Pedestrian Tracking with Shoe-Mounted Inertial Sensors” by E. Foxlin in IEEE Computer Graphics and Applications, Vol. 25, No. 6, Nov./Dec. 2005, pp. 38–46, doi: 10.1109/MCG.2005.140.
• Pedestrian Navigation with IMUs and Other Sensors
“Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors” by P.D. Duong, and Y.S. Suh in Sensors, Vol. 15, No. 7, 2015, pp. 15888–15902, doi: 10.3390/s150715888.
“Enhancing Indoor Inertial Pedestrian Navigation Using a Shoe-Worn Marker” by M. Placer and S. Kovačič in Sensors, Vol. 13, No. 8, 2013, pp. 9836–9859, doi: 10.3390/s130809836.
“Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning” by Z. He, V. Renaudin, M.G. Petovello and G. Lachapelle in Sensors, Vol. 13, No. 4, 2013, pp. 4303–4326, doi: 10.3390/s130404303.
“Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements” by A. Ramón Jiménez Ruiz, F. Seco Granja, J. Carlos Prieto Honorato and J. I. Guevara Rosas in IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, Jan. 2012, pp. 178–189, doi: 10.1109/TIM.2011.2159317.
• Pedestrian Navigation with Kalman Filter Framework
“Indoor Pedestrian Navigation Using an INS/EKF Framework for Yaw Drift Reduction and a Foot-mounted IMU” by A.R. Jiménez, F. Seco, J.C. Prieto and J. Guevara in Proceedings of WPNC’10, the 7th Workshop on Positioning, Navigation and Communication held in Dresden, Germany, March 11–12, 2010, doi: 10.1109/WPNC.2010.5649300.
“A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors” by Z. Xu, J. Wei, B. Zhang and W. Yang in Sensors Vol. 15, No. 4, 2015, pp. 7708–7727, doi: 10.3390/s150407708.
The Association for Unmanned Vehicle Systems International (AUVSI) Xponential 2017 show, May 8-11 in Dallas, convened a global community of commercial and defense suppliers in intelligent robotics, drones and unmanned systems. It showcases the broad forefront of autonomous vehicles generally, but in-flight in particular, and there were plenty of expanded capabilities and expanding applications on display.
In one of several keynotes over the course of the four-day show, Intel Corporation CEO Brian Krzanich predicted that in the oncoming era of driverless cars and autonomous aircraft, the most important aspect of such vehicles will be the data they collect rather than their performance. Big data and cloud processing are somehow tied into UAVs in his vision of things. Sometime soon, he forecast, autonomous devies “will have the ability to make decisions.”
Swarming drones have military potential, according to a 33-year career Marine who now works at the Potomac Institute for Policy Studies. Bill Powers described how a Navy program, the Low-Cost UAV Swarming Technology (LOCUST) uses drones to jam enemy communications and waste its resources by drawing fire. The Naval Research Laboratory dploys Close-in Covert Autonomous Disposable Aircraft (CICADA), with onboard sensors that relay atmospheric conditions as well as possition, time and altitude relating to battlefield conditions.
Watching the Watchers
With all the drones in the air, managing them and keeping the commercial airspace safe and uncluttered has become a towering problem. Several companies at AUVSI introduced unmanned traffic management (UTM) systems.
Unmanned traffic management becoming a priority (image courtesy Gryphon Systems).
Among them, Gryphon Sensors introduced Mobile Skylight, an operational mobile UTM system designed for rapid deployment.
Drone security applications span, according to the company, airport security, critical infrastructure protection, VIP security, embassy protection and border security. In the beyond visual line-of-sight (BVLOS) realm, UTM applications to be enabled by Mobile Skylight include: first responders (EMS, fire and police), precision agriculture, delivery, utility and infrastructure inspection, media and entertainment, mapping and surveying, construction and mining.
In short, everywhere drones go, they will need to be tracked and managed.
Mobile Skylight combines multiple technologies and an array of self-contained sensors, to serve as a mobile command center. The system is provided in a four-wheel drive van with off-road capabilities. It also integrates with third-party sensor inputs, and automatically records essential data for post-mission analysis and playback.
Using a dual-band mesh network, Mobile Skylight is capable of forward deploying a multispectral suite of sensors. Its integrated radar has been designed for 3-D detection of low-flying, small UAS and general aviation at ranges out to 10 kilometers and 27 kilometers, respectively. The system has built-in target tracking and classification to help quickly identify cooperative and non-cooperative targets. It also tracks multiple, simultaneous targets, providing a comprehensive picture of the airspace.
One novel application is heavy-lift drones for the construction and perhaps open-pit mining, quarrying and other weighty sectors. Griff Aviation, a Norwegian company that has set up a manufacturing plant in Florida, displayed its Super Heavy-Lift model, the Griff 300.
Super Heavy-Lift Drone from Griff Aviation image courtesy Gryphon Systems
The GRIFF 300 is an unmanned aircraft with customizable payload options that make it suitable for a variety of professional applications. The company states that it can lift 225kg (496lbs) in addition to its own 75kg (165lbs) weight. It features a flight time of 30-45mins, depending on payload. “The next model that will be produced will be able to lift 800kg (1,764lbs). Then we will continue to increase lifting capacity even further,” said CEO Leif Johan Holand.
Skylift Global drone prepping for flight. image courtesy Gryphon Systems
Several aisles over on the show floor, Skylift Global also featured a drone in the heavy lifting class. “Current prototype is 100 pounds and carried an additional 100 pounds easy. Currently undergoing testing for up to 400 pounds,” says its CEO Amir Emadi.
Skylift has signed agreements with companies in southern California to start deliveries of cold-chain logistics. Its heavy-lift capability can carry the added weight of refrigeration (think Amazon Fresh, says Emadi). Skylift also is in collaboration with JPL and Caltech to showcase a platform to DARPA for autonomous sense and avoid.
Neither company has GPS aboard their workhorses yet but see no problem and plenty of opportunity in adding it as their business develops.
Experienced GNSS Companies
NovAtel had on display its range of high-precision GNSS receivers, antennas, and augmented systems for ground, marine and airborne unmanned applications. Its equipment meets requirements for military and commercial applications, and specific to UAV applications the company offered the OEM625S SAASM GPS+civil RTK receiver, GAJT anti-jam antennas, TerraStar PPP correction services and SPAN GNSS+INS for 3D position, attitude and velocity.
The latter will be featured in the cover story of GPS World’s June issue, differentiating performance of various grades of IMUs in a tightly-coupled inertial/GNSS integration. Exploring IMU specifications and correlating them to performance of a final product can be daunting, as differences between MEMS sensors are not always apparent. The article will present achievable performances in fusion technology across a range of IMUs among the best in their respective performance categories.
Spirent Communications took a dual approach, displaying what they termed an entry-level simulator (although fully upgradeable as needs develop) for UAV manufacturers who are new to GPS signal testing, and even the need for it. They also had on hand their fully configured GSS7000 for multi-frequency testing, also with a modular approach to enable the precision GNSS simulation system to expand with users’ needs.
The GSS7000 series offers emulation of all civil GNSS systems and regional augmentation systems, and allows devices to be tested under a multitude of operating environments and error conditions, the company said. The GSS7000 has the flexibility to reconfigure satellite constellations, channels and frequencies between test runs or test cases. Four software control variants are offered.
More than $20 million for research on unmanned aircraft systems (UAS) was included in an appropriations package that Congress passed and the president signed into law last week to fund the federal government through the end of the fiscal year on Sept. 30. The funding for UAS research is $2.67 million more than last year’s budget request by the Federal Aviation Administration (FAA) to address a host of research challenges associated with integrating UAS into the national airspace system.
The measure’s section on appropriations for transportation agencies also includes $20 million above the 2016 budget request for the FAA’s air traffic control organization. The increase will provide for the hiring and training of new controllers and accelerating UAS airspace integration. The agreement also includes $11.5 million more than was requested for aviation safety activities for UAS integration, including the addition of six full-time positions to support the certification of new technologies and advance the FAA’s organizational delegation authorization (ODA) efforts and strengthen safety oversight.
