Onboard detection and localization of drones using depth maps

A. Carrio, J. Tordesillas Torres, S.H. Vemprala, S. Saripalli, P. Campoy, J.P. How

IEEE Access Vol. 8, pp. 30480 - 30490

Summary:

Obstacle avoidance is a key feature for safe drone navigation. While solutions are already commercially available for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are much harder to develop due to the efficient perception, planning and control capabilities required, particularly in small drones with constrained takeoff weights. For reasonable performance, obstacle detection systems should be capable of running in real-time, with sufficient field-of-view (FOV) and detection range, and ideally providing relative position estimates of potential obstacles. In this work, we achieve all of these requirements by proposing a novel strategy to perform onboard drone detection and localization using depth maps. We integrate it on a small quadrotor, thoroughly evaluate its performance through several flight experiments, and demonstrate its capability to simultaneously detect and localize drones of different sizes and shapes. In particular, our stereo-based approach runs onboard a small drone at 16 Hz, detecting drones at a maximum distance of 8 meters, with a maximum error of 10% of the distance and at relative speeds up to 2.3 m/s. The approach is directly applicable to other 3D sensing technologies with higher range and accuracy, such as 3D LIDAR.

Keywords: Drone, detection, collision avoidance, depth map.

JCR Impact Factor and WoS quartile: 3,367 - Q2 (2020); 3,400 - Q2 (2023)

DOI reference: https://doi.org/10.1109/ACCESS.2020.2971938

Published on paper: 2020.

Published on-line: February 2020.

Citation:
A. Carrio, J. Tordesillas Torres, S.H. Vemprala, S. Saripalli, P. Campoy, J.P. How, Onboard detection and localization of drones using depth maps. IEEE Access. Vol. 8, pp. 30480 - 30490, 2020. [Online: February 2020]