论文标题
姿势校正算法，用于SLAM中密钥框之间的相对帧
Pose Correction Algorithm for Relative Frames between Keyframes in SLAM
论文作者
论文摘要
由于基于密钥帧的SLAM在机器人技术领域的主导地位，通常为更快的算法而牺牲了密钥帧之间的相对框架，以实现在线应用程序。但是，这些方法对于可能需要所有帧的精制姿势的应用程序不足，而不仅仅是与所有输入帧相比相对稀疏的密钥帧。本文提出了一种新颖的算法，以纠正通过后端优化过程更新密钥帧之后的密钥帧之间的相对帧。使用地标和机器人姿势之间的测量约束来得出校正模型。所提出的算法旨在易于集成到现有的基于密钥帧的SLAM系统中，同时表现出优于现有插值方法的鲁棒和准确的性能。该算法还需要低计算资源，因此在整个SLAM管道上的负担最小。与各种矢量空间中的现有插值方法相比，我们提供了对拟议的姿势校正算法的评估，我们的方法在KITTI和EUROC数据集中都表现出了极好的准确性。
With the dominance of keyframe-based SLAM in the field of robotics, the relative frame poses between keyframes have typically been sacrificed for a faster algorithm to achieve online applications. However, those approaches can become insufficient for applications that may require refined poses of all frames, not just keyframes which are relatively sparse compared to all input frames. This paper proposes a novel algorithm to correct the relative frames between keyframes after the keyframes have been updated by a back-end optimization process. The correction model is derived using conservation of the measurement constraint between landmarks and the robot pose. The proposed algorithm is designed to be easily integrable to existing keyframe-based SLAM systems while exhibiting robust and accurate performance superior to existing interpolation methods. The algorithm also requires low computational resources and hence has a minimal burden on the whole SLAM pipeline. We provide the evaluation of the proposed pose correction algorithm in comparison to existing interpolation methods in various vector spaces, and our method has demonstrated excellent accuracy in both KITTI and EuRoC datasets.
