Reciprocal Visibility for Guided Occlusion Removal with Drones

Kurzfassung

In this paper, a guidance strategy is proposed to optimize synthetic aperture sampling for occlusion removal with drones based on point-cloud representation of occluders. Pre-recorded LiDAR scans are utilized to compute the visibility of fixed inspection regions on the ground that are intended for recurrent monitoring from the air. By utilizing Helmholtz reciprocity, the drone-collected LiDAR scans are used to computationally obtain a reciprocal visibility of potential drone positions in the air from points of interest on the ground. This visibility forms a basis for a novel navigation strategy. This strategy was shown to drive drones to optimal aerial monitoring positions, thus reducing occlusion and consequently the sampling time. Compared to previous unguided sampling, we achieve a 5-20% higher visibility with 9-17 times less samples in our experiments.