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.

中文翻译：

---

基于立体视觉的物体检测，用于空间环境中的自主导航

障碍物检测和规避是部分已知或未知环境自主导航的主要问题。随着太空碎片的扩散，研究人员正在积极研究碎片清除，以促进未来的太空行动。这就需要开发用于太空任务的自主导航技术。自由空间物体检测是智能系统中的一项关键任务，特别是对于路径规划。在这项研究中，我们提出了一种基于立体视觉的空间物体检测智能系统，使用两个相隔 10 厘米垂直排列的全向立体相机。首先，采用基于深度学习的单次多框检测器（SSD）来识别图像中存在的对象。然后，利用三角测量方法确定物体与系统之间的距离。所提出的系统可以在太空环境中提供最大范围为1.1公里的物体深度信息。