利用邻近点进行鲁棒姿势图优化

Pub Date : 2023-12-20 DOI:10.20965/jrm.2023.p1480

Y. Tazaki, Kotaro Wada, Hikaru Nagano, Yasutoshi Yokokohji

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引用次数: 0

摘要

本文提出了一种针对有关键点的姿势图的鲁棒姿势图优化（PGO）方法。在传统的 PGO 方法中，循环约束是在一对节点之间定义的，而在本文提出的方法中，循环约束是在一对关键点之间定义的。通过这种方式，基于开关变量的稳健 PGO 可以以更精细的方式实现。循环约束是根据临近点的独特几何特性定义的，并作为 g2o 求解器的一种新边缘类型来实现。利用 2021 年中之岛机器人挑战赛中记录的真实数据，将所提出的方法与其他鲁棒 PGO 方法进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Robust Posegraph Optimization Using Proximity Points

This paper proposes a robust posegraph optimization (PGO) method for posegraphs with keypoints. In the conventional PGO formulation, a loop constraint is defined between a pair of nodes, whereas in the proposed method, it is defined between a pair of keypoints. In this manner, robust PGO based on switch variables can be realized in a more fine-grained manner. Loop constraint is defined based on the unique geometric property of proximity point, and implemented as a new edge type of the g2o solver. The proposed method is compared with other robust PGO methods using real world data recorded in Nakanoshima Robot Challenge 2021.

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