Intersection status check table.

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.

定位是自主移动机器人面临的关键挑战之一，平整墙体则是室内定位的一类基础参考基准。在诸多实际场景中，可获取墙体表面平面的先验知识，例如建筑信息模型（BIM）系统中的平面数据。本文提出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对移动机器人的位姿进行估计。本文提出一种扩展图像坐标系，用于表征空间中任意平面，并建立可见平面与世界坐标系中平面的对应关系。针对实时点云中表征约束平面的潜在可见点，利用由扩展图像坐标系内理论可见平面区域导出的感兴趣区域（ROI）滤波器完成筛选。平面对应点的数量会影响多平面定位方法中的计算权重。经实验验证，所提定位方法可在初始位姿存在误差冗余的情况下实现有效定位。