Stereo vision based object detection for autonomous navigation in space environments

Abstract

Obstacle detection and avoidance are the major issues in autonomous navigation for partially known or unknown environments. With the proliferation of space debris, researchers are actively investigating debris removal to facilitate future space operations. This calls for the development of autonomous navigation techniques for space missions. Free-space object detection is a crucial task in intelligent systems, particularly for path planning. In this study, we propose a stereo vision-based intelligent system for space object detection, using two vertically aligned omnidirectional stereo cameras separated by 10 cm. Firstly, a single-shot multibox detector (SSD) based on deep learning is employed to identify the objects present in the image. Then, the triangulation method is used to determine the distance between the object and the system. The proposed system can provide object depth information up to a maximum range of 1.1 km in a space environment.