Department of Mechanical Engineering, Tsinghua University, Beijing, China.
Moscow Engineering Physics Institute, National Research Nuclear University MEPhI, Moscow, Russia.
PLoS One. 2023 May 8;18(5):e0285509. doi: 10.1371/journal.pone.0285509. eCollection 2023.
Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall's surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.
本地化对于自主移动机器人来说是一个关键的挑战，而平坦的墙壁则是室内定位的基本参考。在许多场景中，墙壁表面的平面是已知的，例如建筑信息建模 (BIM) 系统中的平面。本文提出了一种基于先验平面点云提取的定位技术。通过实时多平面约束来估计移动机器人的位置和姿态。提出了一个扩展的图像坐标系来表示空间中的任意平面，并建立可见平面与世界坐标系中平面之间的对应关系。通过使用感兴趣区域（ROI）过滤实时点云中表示约束平面的潜在可见点，该 ROI 源自扩展图像坐标系中理论可见平面区域。表示平面的点数会影响多平面定位方法中的计算权重。所提出的定位方法的实验验证表明，它允许在初始位置和姿态误差方面存在冗余。
