Foes or Friends: Embracing Ground Effect for Edge Detection on Lightweight Drones

Chenyu Zhao, Ciyu Ruan, Jingao Xu, Haoyang Wang, Shengbo Wang, Jiaqi Li, Jirong Zha, Zheng Yang, Yunhao Liu, Xiao-Ping Zhang, Xinlei Chen

Abstract: Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radar or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe" in traditional flight control views, into a "friend" for accurate and efficient edge detection. Our key insight is that analyzing drone sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over an edge, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline performance by 86%.

External IDs:doi:10.1145/3636534.3690699