Weighted total least squares–Bayes filter-based estimation of relative pose for a space non-cooperative unknown target without a priori knowledge

Abstract

Abstract The estimation of relative pose of a space non-cooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the accuracy of the estimation and the robustness of the filter algorithms. In response, this paper proposes a novel filter algorithm for estimating relative pose to improve robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as an observation quantity. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependence on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.