We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid body (or "target"), relative to its own, without the assistance of external infrastructure, without prior knowledge of the shape of the target, and taking into account the possibility that the available observations are incomplete. Three complementary contributions are then offered for such a scenario. The first is a method to estimate the translation vector between the center point of both rigid bodies, which unlike existing techniques does not require that both objects have the same shape or even the same number of landmark points. This technique is shown to significantly outperform the state-of-the-art (SotA) under complete information, but to be sensitive to data erasures, even when enhanced by matrix completion methods. The second contribution, designed to offer improved performance in the presence of incomplete information, offers a robust alternative to the latter, at the expense of a slight relative loss under complete information. Finally, the third contribution is a scheme for the estimation of the rotation matrix describing the relative orientation of the target rigid body with respect to the primary. Comparisons of the proposed schemes and SotA techniques demonstrate the advantage of the contributed methods in terms of root mean square error (RMSE) performance under fully complete information and incomplete conditions.

翻译：本文研究刚体定位问题的一种鲁棒且自依赖（或称"自中心"）变体，其中主体刚体旨在估计另一刚体（即"目标"）相对于自身位姿（即位置与朝向），且无需外部基础设施辅助、无需预先获知目标形状，并考虑可用观测数据可能不完整的情况。针对该场景，我们提出三项互补性贡献。首先是估计两个刚体中心点间平移向量的方法，与现有技术不同，该方法不要求两物体具有相同形状甚至相同数量的地标点。在完整信息条件下，该技术显著优于现有最优方法，但对数据缺失敏感，即使采用矩阵补全方法增强后亦然。第二项贡献旨在提升不完整信息条件下的性能，为此提供了一种鲁棒替代方案，代价是在完整信息条件下存在轻微的相对性能损失。最后，第三项贡献是估计描述目标刚体相对于主体刚体相对朝向的旋转矩阵的方案。所提方案与现有最优技术的对比实验表明，在完全完整信息与不完整条件下，所提方法在均方根误差性能方面均具有优势。