Abstract
Existence of symmetric objects, whose observation at different viewpoints can be identical, can deteriorate the performance of simultaneous localization and mapping(SLAM). This work proposes a system for robustly optimizing the pose of cameras and objects even in the presence of symmetric objects. We classify objects into three categories depending on their symmetry characteristics, which is efficient and effective in that it allows to deal with general objects and the objects in the same category can be associated with the same type of ambiguity. Then we extract only the unambiguous parameters corresponding to each category and use them in data association and joint optimization of the camera and object pose. The proposed approach provides significant robustness to the SLAM performance by removing the ambiguous parameters and utilizing as much useful geometric information as possible. Comparison with baseline algorithms confirms the superior performance of the proposed system in terms of object tracking and pose estimation, even in challenging scenarios where the baseline fails.
Abstract (translated)
存在对称对象,其在不同视角下的观察结果完全相同,可能会恶化同时定位和映射(SLAM)的性能。该研究提出了一种系统,能够在存在对称对象的情况下, robustly 优化相机和对象的姿态。我们将对象按照其对称性特征分为三个类别,这种方法既高效又有效,因为它可以处理一般对象,同一类别中的 objects 可以具有相同的歧义。然后,我们只提取每个类别中的无歧义参数,并将其用于相机和对象姿态的数据关联和联合优化。该方法提供了对 SLAM 性能的重大鲁棒性,通过去除歧义参数并尽可能利用有用的几何信息。与基准算法进行比较确认了 proposed 系统在对象跟踪和姿态估计方面的优势,即使在基准算法失败的情况下也是如此。
URL
https://arxiv.org/abs/2303.07872
