在轨服务对象位姿确定问题的研究现状分析

O. Fokov
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摘要

近年来,在轨服务对象的姿态估计问题受到了广泛的关注。近距离的姿态确定仍然是一个开放的研究领域,特别是对于在轨服务的非合作对象(目标)。本文的目标是概述在轨服务对象相对运动参数确定问题的最新研究进展,重点介绍了具有非合作和未知目标的近距离操作。所采用的方法是分析过去十年专门讨论这个问题的出版物。分析结果如下。利用视频系统确定非合作轨道物体的姿态是一种经典的方法,因为它具有重量轻、功耗低的优点。基于摄像机的姿态估计算法通常需要事先了解目标特征。姿态估计的主要方法仍然是基于连续帧或目标模型的图像特征识别和对应的方法。另一种主要的姿态确定方法是激光雷达导航,其中基于激光雷达导出的目标表面点云的特征识别和对应也是常见的方法。近年来,包括未知目标在内的目标姿态确定的非特征方法发展成为一种趋势。激光雷达点云数据的三维特性有利于在没有目标模型的情况下进行目标位姿估计。在目标位姿估计方法对未知目标的适用性方面,在估计目标的空间运动之前,先对目标的一系列图像进行处理,建立目标的三维模型,实现明显方法需要大量的时间,这在近距离操作中是至关重要的。目标姿态估计的发展趋势是同时估计未知物体的姿态和形状的方法。一般来说,不明物体的情况尚未得到充分调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the state of the art in the problem of determining the pose of on-orbit service objects
Recently considerable attention has been paid to the problem of estimating the pose of an on-orbit service object. Determining the pose at a close distance still remains an open line of research, especially for non-cooperative objects (targets) of on-orbit service. The goal of this work is to overview the state of the art in the problem of determining the relative motion parameters of on-orbit service objects with emphasis on close proximity operations with non-cooperative and unknown targets. The method employed is the analysis of publications devoted to this problem over the last decade. The analysis showed the following. Determining the pose of a non-cooperative orbital object using video systems is a classical approach due to the advantages of light weight and low power consumption. Video camera based pose estimation algorithms usually require prior knowledge of the target features. The main methods of pose estimation still involve approaches based on the recognition and correspondence of image features for consecutive frames or with a target model. Another major approach to pose determination is lidar navigation, where the recognition and correspondence of features based on lidar-derived target surface point clouds are also common methods. Recently, a trend has emerged towards the development of non-feature methods for target pose determination, including unknown targets. The three-dimensional nature of lidar point cloud data is favorable for target pose estimation without any target model. As to the applicability of target pose estimation methods to an unknown target, the implementation of the obvious approach based on constructing a three-dimensional model of the target by processing a series of its images prior to estimating its spatial motion takes a lot of time, which is critical in close proximity operations. The trend in target pose estimation is the development of methods for simultaneous estimation of the pose and shape of an unknown object. In general, the case of an unknown object has not yet been fully investigated.
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