发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对建筑工地低纹理和相似布局导致的定位难题，提出基于Transformer的LiDAR全局重定位系统，结合PointNet++与位置识别，在MCL框架中实现鲁棒定位，合成数据训练，推理快且优于现有基线。
AI 中文摘要
为了执行检查或数字化任务，建筑工地上的移动机器人必须能够可靠地相对于与建筑地图共享的全局参考系进行定位。相似的房间布局和低纹理表面对现有的基于LiDAR和视觉的定位方法构成了挑战。我们通过一种基于LiDAR的全局重定位系统来解决这个问题，该系统估计机器人相对于建筑网格的位姿，并将PointNet++编码器与位置识别解码器相结合，其输出在蒙特卡洛定位（MCL）框架内充当学习到的观测模型。该流程仅在通过模拟机器人传感器在建筑网格内获得的合成LiDAR扫描上进行训练。由于具有不确定性感知的解码器（可缩放位置似然）和一种将模型假设注入粒子集的重新采样策略，我们的方法在模糊环境中具有鲁棒性，能够从潜在的粒子耗尽中恢复。在真实世界数据集上的评估表明，我们的方法优于基于扩散的方法和ScanContext++基线，同时保持快速推理（每次调用18毫秒），证明了合成数据训练在建筑机器人网格参考全局定位中的实用性。
英文摘要
To be able to perform inspection or digitization tasks, mobile robots on construction sites must be able to localize themselves reliably with respect to a global reference frame that is shared with a building map. Similar room layouts and low-texture surfaces pose a challenge for existing LiDAR- and vision-based localization methods. We approach this problem with a LiDAR-based global relocalization system that estimates the robot's pose relative to a building mesh and combines a PointNet++ encoder with a place recognition decoder, whose outputs serve as a learned observation model within a Monte Carlo Localization (MCL) framework. The pipeline is trained exclusively on synthetic LiDAR scans obtained by simulating the robot's sensors inside the building mesh. Our approach is robust in ambiguous environments due to an uncertainty-aware decoder that scales positional likelihoods and a resampling strategy that injects model hypotheses into the particle set, enabling recovery from potential particle depletion. Evaluations on real-world datasets show that our method outperforms both diffusion-based and ScanContext++ baselines while maintaining fast inference (18 ms per call), demonstrating the practicality of synthetic-data training for mesh-referenced global localization in construction robotics.
