Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs

Khaghani, Mehran; Skaloud, Jan

doi:10.1002/navi.140

Cited by 43 publications

(38 citation statements)

References 16 publications

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“…GPS with INS aiding has been applied, along with other sensors, to multiple vehicular and UAV applications and has been shown to provide position estimates where GPS is intermittently unavailable. Khagani et al have demonstrated that fusing GPS/INS with a vehicle dynamic model leads to major improvements in navigation accuracy in periods of GPS outages . Klein et al rely on a navigation solution and use pseudorange measurements as aiding to navigate periods of GPS outages.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen

Gao

2019

NAVIGATION

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Because of the recent interest in unmanned air vehicle (UAV) commercialization, there is a great need for navigation algorithms that provide accurate and robust positioning in urban environments that are often Global Positioning Systems (GPS) challenged or denied. In this paper, we present a probabilistic graph-based navigation algorithm resilient to GPS errors. Fusing GPS pseudorange and Light Detection and Ranging (LiDAR) odometry measurements with 3D building maps, we apply a batch estimation approach to generate a robust trajectory estimate and maps of the surrounding environment. We then leverage the maps to locate potential sources of GPS multipath and mitigate the effects of degraded pseudorange measurements on the trajectory estimate. We experimentally validate our results with flight tests conducted in GPS-challenged and GPS-denied environments.

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Section: Related Workmentioning

confidence: 99%

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen

Gao

2019

NAVIGATION

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“…In addition, each UAV is assumed to be loaded with the same magnetic anomaly map. Meanwhile, each UAV is assumed to be able to measure or estimate its velocity and yaw rate through some methods, such as using Vehicle Dynamic Model (VDM) presented in [37] or Visual Odometry presented in [38,39]. No additional information is used for UAV navigation in this study.…”

Section: Problem Statementmentioning

confidence: 99%

Cooperative UAV Navigation using Inter-Vehicle Ranging and Magnetic Anomaly Measurements

Yang

Strader

et al. 2018

2018 AIAA Guidance, Navigation, and Control Conference

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The problem of cooperative localization for a small group of unmanned aerial vehicles (UAVs) in a Global Navigation Satellite System-denied environment is addressed in this paper. The presented approach containstwo sequential steps: first, an algorithm called cooperative ranging localization, formulated as an extended Kalman filter, estimates each UAV's relative pose inside the group using intervehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV's global pose through matching the group's magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 m after flying about 180 km in 1 h. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high-and low-resolution maps as more UAVs are added into the group.

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“…In , the authors apply the VDM approach using a completely different perspective. They use the IMU as observation not as the main process model, while the VDM is used as the main process model, so any additional data available from IMU or GNSS act as observations for the VDM model.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Machine Learning VDM for UAVs in GNSS-denied Environment

et al. 2018

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This paper presents a novel approach to enhance unmanned aerial vehicle (UAV) autonomous navigation, without adding extra load to the vehicle. The proposed approach employs the UAV vehicle dynamic model to aid the navigation estimation in a Global Navigation Satellite Systems (GNSS)‐denied environment, without the need to model any part of the UAV, and avoids the requirement for special equipment during the modeling procedures typically required for vehicle dynamic model‐aided navigation. Taking advantage of the available information from previous flights during availability of GNSS, and with the aid of a hybrid machine learning approach, the proposed technique is able to enhance the navigation accuracy during GNSS outage, despite the massive drift occurring from utilizing a low‐cost inertial measurement unit (IMU) during the outage period. Different scenarios are investigated to prove the robustness of the proposed technique, and the results are compared to two types of IMUs with the aid of an inverse mechanization IMU simulator. © 2018 Institute of Navigation

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Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs

Cited by 43 publications

References 16 publications

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Cooperative UAV Navigation using Inter-Vehicle Ranging and Magnetic Anomaly Measurements

Hybrid Machine Learning VDM for UAVs in GNSS-denied Environment

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