Vision-Based Autonomous Landing on Unprepared Field With Rugged Surface

Landing on unprepared terrains is a challenging task for fully autonomous micro unmanned aerial vehicles (UAVs). Most of the existing methods mainly rely on manual control when the terrain of the target area is unknown in advance. In this article, we propose an autonomous landing method based on a learning-based multi-view stereo (MVS) system. UAV acquires multiple RGB pictures of the terrain after cruise, and then extracts speed-up robust features (SURF) and perform structure from motion (SFM) to obtain sparse feature point clouds. By utilizing the generated initial depth map, we further propose a novel 3-D reconstruction algorithm named PatchmatchNet-A, which can help to obtain precise and stable estimates of the depth information. In PatchmatchNet-A, we also use a new activation function, adjustable-arctangent linear units (ALU), to improve the accuracy and robustness. We have tested our algorithms in a DTU dataset and found that it can speed up the processing by around 25% while guaranteeing competitive performance. Flight experiments have also been conducted to verify the effectiveness of the whole landing system by using a commercial M210V2 quadcopter.