High-precision and real-time algorithms of multi-object detection, recognition and localization toward ARVD of cooperative spacecrafts

Abstract

The spacecraft pose estimation based on vision measurement is widely used as an effective method for proximity operation in autonomous rendezvous and docking (ARVD). In order to estimate pose parameters more effectively, it is necessary to achieve high-precision object localization in video images. However, there are some difficulties to satisfy precision and real-time requirements simultaneously for the localization of multiple objects. In this paper, high-precision and real-time algorithms are proposed for multi-object detection, recognition and localization toward ARVD of cooperative spacecrafts. At first, through the denoise and characteristic analysis of video images, an object detection method is proposed based on adaptive threshold segmentation by threshold optimization with the variances of inter-classes and intra-class; then a recognition algorithm of multi-object is proposed by self-organization clustering; moreover, a high-precision localization algorithm of multi-object is proposed based on bilinear interpolation, which can achieve sub-pixel precision for object centroid estimation. A series of experimental results demonstrate that the proposed algorithms can achieve excellent object detection accuracy of 100% within 120m, high-precision object localization of 0.004–0.43pixel at 0.3–150m distance, and is also characterized with strong anti-disturbance and well real-time of 22ms average runtime so as to make a sound basis for further engineering tests.