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At least 181 records · Page 10

Synchronized Flashing Lights For Approach And Docking

Proposed optoelectronic system for guiding vehicle in approaching and docking with another vehicle includes active optical targets (flashing lights) on approached vehicle synchronized with sensor and image-processing circuitry on approaching vehicle. Conceived for use in automated approach and docking of two spacecraft. Also applicable on Earth to manually controlled and automated approach and docking of land vehicles, aircraft, boats, and submersible vehicles, using GPS or terrestrial broadcast time signals for synchronization. Principal advantage: optical power reduced, with consequent enhancement of safety.

Book, Michael L.↗

Autonomous Relative Navigation for Formation-Flying Satellites Using GPS

The Goddard Space Flight Center is currently developing advanced spacecraft systems to provide autonomous navigation and control of formation flyers. This paper discusses autonomous relative navigation performance for a formation of four eccentric, medium-altitude Earth-orbiting satellites using Global Positioning System (GPS) Standard Positioning Service (SPS) and "GPS-like " intersatellite measurements. The performance of several candidate relative navigation approaches is evaluated. These analyses indicate that an autonomous relative navigation position accuracy of 1meter root-mean-square can be achieved by differencing high-accuracy filtered solutions if only measurements from common GPS space vehicles are used in the independently estimated solutions.

Gramling, Cheryl↗

Multi-Flight-Phase GPS Navigation Filter Applications to Terrestrial Vehicle Navigation and Positioning

A candidate onboard space navigation filter demonstrated excellent performance (less than 8 meter level RMS semi-major axis accuracy) in performing orbit determination of a low-Earth orbit Explorer satellite using single-frequency real GPS data. This performance is significantly better than predicted by other simulation studies using dual-frequency GPS data. The study results revealed the significance of two new modeling approaches evaluated in the work. One approach introduces a single-frequency ionospheric correction through pseudo-range and phase range averaging implementation. The other approach demonstrates a precise axis-dependent characterization of dynamic sample space uncertainty to compute a more accurate Kalman filter gain. Additionally, this navigation filter demonstrates a flexibility to accommodate both perturbational dynamic and observational biases required for multi-flight phase and inhomogeneous application environments. This paper reviews the potential application of these methods and the filter structure to terrestrial vehicle and positioning applications. Both the single-frequency ionospheric correction method and the axis-dependent state noise modeling approach offer valuable contributions in cost and accuracy improvements for terrestrial GPS receivers. With a modular design approach to either 'plug-in' or 'unplug' various force models, this multi-flight phase navigation filter design structure also provides a versatile GPS navigation software engine for both atmospheric and exo-atmospheric navigation or positioning use, thereby streamlining the flight phase or application-dependent software requirements. Thus, a standardized GPS navigation software engine that can reduce the development and maintenance cost of commercial GPS receivers is now possible.

Park, Young W.↗

Spaceborne GPS: Current Status and Future Visions

The Global Positioning System (GPS), developed by the Department of Defense is quickly revolutionizing the architecture of future spacecraft and spacecraft systems. Significant savings in spacecraft life cycle cost, in power, and in mass can be realized by exploiting GPS technology in spaceborne vehicles. These savings are realized because GPS is a systems sensor--it combines the ability to sense space vehicle trajectory, attitude, time, and relative ranging between vehicles into one package. As a result, a reduced spacecraft sensor complement can be employed and significant reductions in space vehicle operations cost can be realized through enhanced on-board autonomy. This paper provides an overview of the current status of spaceborne GPS, a description of spaceborne GPS receivers available now and in the near future, a description of the 1997-2000 GPS flight experiments, and the spaceborne GPS team's vision for the future.

Bauer, Frank H.↗

Spaceborne GPS Current Status and Future Visions

The Global Positioning System (GPS), developed by the Department of Defense, is quickly revolutionizing the architecture of future spacecraft and spacecraft systems. Significant savings in spacecraft life cycle cost, in power, and in mass can be realized by exploiting Global Positioning System (GPS) technology in spaceborne vehicles. These savings are realized because GPS is a systems sensor-it combines the ability to sense space vehicle trajectory, attitude, time, and relative ranging between vehicles into one package. As a result, a reduced spacecraft sensor complement can be employed on spacecraft and significant reductions in space vehicle operations cost can be realized through enhanced on- board autonomy. This paper provides an overview of the current status of spaceborne GPS, a description of spaceborne GPS receivers available now and in the near future, a description of the 1997-1999 GPS flight experiments and the spaceborne GPS team's vision for the future.

Bauer, Frank H.↗

Single-Frequency GPS Relative Navigation in a High Ionosphere Orbital Environment

The Global Positioning System (GPS) provides a convenient source for space vehicle relative navigation measurements, especially for low Earth orbit formation flying and autonomous rendezvous mission concepts. For single-frequency GPS receivers, ionospheric path delay can be a significant error source if not properly mitigated. In particular, ionospheric effects are known to cause significant radial position error bias and add dramatically to relative state estimation error if the onboard navigation software does not force the use of measurements from common or shared GPS space vehicles. Results from GPS navigation simulations are presented for a pair of space vehicles flying in formation and using GPS pseudorange measurements to perform absolute and relative orbit determination. With careful measurement selection techniques relative state estimation accuracy to less than 20 cm with standard GPS pseudorange processing and less than 10 cm with single-differenced pseudorange processing is shown.

Conrad, Patrick R.↗

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (&deg;C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a &ldquo;0&rdquo; if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the &ldquo;time warp&rdquo; period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046&ndash;1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503&ndash;2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100&ndash;103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278&ndash;283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. Here, to address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

33 ADVANCED PROPULSION SYSTEMS↗

Puget Sound Regional Council 2006 Household Activity Survey

The purpose of the 2006 Puget Sound Regional Council Household Activity Survey was to provide data for travel demand models for the Puget Sound region, for the assessment of current activity and travel patterns, and for the estimation of future activity and travel within the region under various policy scenarios. One important goal of this project is to improve planners' ability to evaluate impacts of future policies and actions on travel patterns and transportation facility use through the development of a database. This database both captures the current status of activity and travel in the region and includes attitudes, preferences, and choices about activities and travel. In the design of the 2006 survey, basic demographics, activities, and tour and travel characteristics were collected for every member (including children) of 4,746 households during a consecutive 48-hour travel period. Vehicle global positioning system (GPS) data were collected from a subsample of 220 of these households, including completed activity/travel diaries for each household member. (Up to three vehicles per household were equipped with GPS units). The final GPS tracking data contained detailed information on the travel paths of 220 households with two vehicles in the same 48-hour period recorded in the diaries.

1Hz data↗

Implementation of a low-cost, commercial orbit determination system

This paper describes the implementation and potential applications of a workstation-based orbit determination system developed by Storm Integration, Inc. called the Precision Orbit Determination System (PODS). PODS is offered as a layered product to the commercially-available Satellite Tool Kit (STK) produced by Analytical Graphics, Inc. PODS also incorporates the Workstation/Precision Orbit Determination (WS/POD) product offered by Van Martin System, Inc. The STK graphical user interface is used to access and invoke the PODS capabilities and to display the results. WS/POD is used to compute a best-fit solution to user-supplied tracking data. PODS provides the capability to simultaneously estimate the orbits of up to 99 satellites based on a wide variety of observation types including angles, range, range rate, and Global Positioning System (GPS) data. PODS can also estimate ground facility locations, Earth geopotential model coefficients, solar pressure and atmospheric drag parameters, and observation data biases. All determined data is automatically incorporated into the STK data base, which allows storage, manipulation and export of the data to other applications. PODS is offered in three levels: Standard, Basic GPS and Extended GPS. Standard allows processing of non-GPS observation types for any number of vehicles and facilities. Basic GPS adds processing of GPS pseudo-ranging data to the Standard capabilities. Extended GPS adds the ability to process GPS carrier phase data.

Corrigan, Jim↗

GPS Measurement Of Attitude

Signals transmitted by satellites of Global Positioning System (GPS) measure orientation of baseline on ship, aircraft, or other vehicle with accuracy. Two GPS antennas and receivers placed at well separated points on platform. Receivers measure positions of ends of baseline as functions of time. Output processor computes vector difference between two positions and determines orientation of baseline. Combined with conventional GPS data, orientation data allows more precise navigation and mapping and enhances calculations related to performance and control of vehicle.

Dinardo, S. J.↗

GPS compound eye attitude and navigation sensor and method

The present invention is a GPS system for navigation and attitude determination, comprising a sensor array including a convex hemispherical mounting structure having a plurality of mounting surfaces, and a plurality of antennas mounted to the mounting surfaces for receiving signals from space vehicles of a GPS constellation. The present invention also includes a receiver for collecting the signals and making navigation and attitude determinations. In an alternate embodiment the present invention may include two opposing convex hemispherical mounting structures, each of the mounting structures having a plurality of mounting surfaces, and a plurality of antennas mounted to the mounting surfaces.

Quinn, David A.↗

FleetREDI Dashboard Fleet DNA Data Summaries

Developing daily duty cycle summaries for every vehicle-day within NLR’s Fleet DNA database was a key output of the FleetREDI project. This project captured second-by-second GPS and controller area network (CAN) data on in-use medium- and heavy-duty fleet vehicles and then summarized the data to provide an overview of vehicle operation throughout the United States. These data summaries were then displayed in aggregated formats on the FleetREDI dashboard, where users can explore the data within Fleet DNA. Fleet DNA’s clearinghouse of commercial fleet vehicle operating data helps vehicle manufacturers and developers optimize vehicle designs and helps fleet managers choose advanced technologies for their fleets. This online tool, which provides data summaries and visualizations similar to real-world "genetics" for medium- and heavy-duty fleet vehicles, helps users understand the broad operational range of commercial vehicles across vocations and weight classes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2004 Kansas City Regional Household Travel Survey

The 2004 Regional Household Travel Survey documented the travel behavior characteristics of Kansas City residents to update the area's transportation model. The Mid-America Regional Council and the Kansas and Missouri Departments of Transportation sponsored the survey, which was administered by NuStats. Activity and travel information was collected for all household members, regardless of age, during a specific 24-hour period. It relied on the willingness of households to provide demographic information about its members and vehicles and to have all household members record all travel and activity for a specific 24-hour period. The study also included a subsample of household vehicles with global positioning system (GPS) devices. The objectives of the GPS component were twofold: (1) to provide an independent data stream of vehicular travel in order to measure the accuracy of the travel data reported over the telephone, and (2) to obtain details about those trips that were captured by GPS but not reported over the telephone, in order to derive a trip-correction factor.

1Hz data↗

Cobalt: Terrestrial Flight Test of Landing Navigation Using Lander Vision System with Navigation Doppler Lidar

COBALT (CoOperative Blending of Autonomous Landing Technologies) is a NASA technology development and test program to advance precision landing capabilities for future soft landers. The COBALT payload demonstrated terrain relative navigation utilizing the Lander Vision System and Navigation Doppler Lidar sensors on the Masten Space Systems Xodiac rocket. In spring 2017, the program culminated in two open-loop free flights to 500 m altitude with downrange diverts of 300 m. The COBALT system performed well, navigating within meters of the Xodiac vehicle’s GPS-based solution. This paper outlines details of the navigation filter and its performance during the test campaign.

Carson, John M. III↗

2011 Atlanta, Georgia, Regional Travel Survey

The 2011 Regional Travel Survey collected trip data from households across 20 counties in and around Atlanta, Georgia, to improve regional travel demand forecasts. The survey was conducted by PTV NuStats, GeoStats, and PG Americas Inc. on behalf of the Atlanta Regional Commission. The goal was to collect trip data from a minimum of 10,000 households with a subsample of 1,000 households providing global positioning system (GPS) data. The GPS add-on consisted of two sample groups. The first sample group was provided with GPS devices to install in their personal vehicles (797 vehicles) to collect the speed and location of a vehicle during an assigned study period. The second sample group (1,653 participants) was provided with a wearable GPS device to collect the speed and location of an individual during an assigned study period. People participating in the wearable add-on part of the study were chosen because their travel diaries reported use of public transit in day-to-day travel. Each GPS participant was grouped into either the spring phase (March to May 2011) or the fall phase (July to September 2011), and data were recorded for a maximum of seven days for vehicle units and three days for wearable units.

1Hz data↗

GPS Lessons Learned from the International Space Station, Space Shuttle and X-38

This document is a collection of writings concerning the application of Global Positioning System (GPS) technology to the International Space Station (ISS), Space Shuttle, and X-38 vehicles. An overview of how GPS technology was applied is given for each vehicle, including rationale behind the integration architecture, and rationale governing the use (or non-use) of GPS data during flight.

Goodman, John L.↗

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

Wedescribeanew,on-board,shortrangeperceptionsystem that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

Matthies, Larry↗