Obstacle regions extraction method for unmanned aerial vehicles based on space–time tensor descriptor

Abstract

Obstacle avoidance is an important and challenging task for the autonomous flight of unmanned aerial vehicles. Obstacle regions extraction from image sequences is a critical prerequisite in obstacle avoidance. We propose an obstacle regions extraction method based on space–time tensor descriptor. In our method, first, the space–time tensor descriptor is defined and a criterion function based on the descriptor of extracting space–time interest points (STIPs) is designed. Then a self-adaptive clustering of STIPs approach is presented to locate the possible obstacle regions. Finally, an improved level set algorithm is applied with the result of clustering to extract the obstacle regions. We demonstrate the experiments of obstacle regions extraction by our method on image sequences. Sequences are captured in indoor simulative obstacle avoidance environments and outdoor real flight obstacle avoidance environments. Experimental results validate that our method can effectively complete extraction and segmentation of obstacle region with captured images. Compared with the state-of-the-art methods, our method performs well to extract the contours of obstacle regions on the whole and significantly improves segmentation speed.