论文标题
建筑3D计划中的精确机器人本地化
Precise Robot Localization in Architectural 3D Plans
论文作者
论文摘要
本文提出了一种用于移动机器人的本地化系统，可在不准确的建筑模型中进行精确定位。该方法利用本地参考来抵消本规划和本规范数据之间的固有偏差，以进行本地准确的注册。我们进一步将基于图像的新型鲁棒离群检测器与LIDAR数据融合在一起，以拒绝杂物，动态对象和传感器故障的广泛异常测量。我们在充满挑战的现实世界建筑工地中评估了移动机器人的拟议方法。它始终优于传统的基于ICP的主管，将本地化误差降低至少30％。
This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel image-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. We evaluate the proposed approach on a mobile robot in a challenging real world building construction site. It consistently outperforms the traditional ICP-based alingment, reducing localization error by at least 30%.
