TY - GEN
T1 - Tracking drone orientation with multiple GPS receivers
AU - Gowda, Mahanth
AU - Manweiler, Justin
AU - Dhekne, Ashutosh
AU - Choudhury, Romit Roy
AU - Weisz, Justin D.
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/3
Y1 - 2016/10/3
N2 - Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a failsafe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters - a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.
AB - Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a failsafe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters - a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.
KW - Carrier phases
KW - Differential GPS
KW - Drones
KW - IMU
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=84994169082&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994169082&partnerID=8YFLogxK
U2 - 10.1145/2973750.2973768
DO - 10.1145/2973750.2973768
M3 - Conference contribution
AN - SCOPUS:84994169082
SN - 9781450342261
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 280
EP - 293
BT - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
PB - Association for Computing Machinery
T2 - 22nd Annual International Conference on Mobile Computing and Networking, MobiCom 2016
Y2 - 3 October 2016 through 7 October 2016
ER -