用扩散模型提升工地激光雷达定位精度，解决相似场景下的混淆问题。
Diffusion Based Robust LiDAR Place Recognition
- 基于合成点云训练扩散模型，从单帧激光数据推断多候选位置。
- 在五个真实数据集上实现77%的定位准确率（误差±2米），均方误差降低两倍。
- 适合复杂重复结构环境中的机器人全局定位，尤其适用于建筑工地。
建筑工地中的移动机器人需要精确位姿估计以完成自主测绘与巡检任务。由于存在大量重复特征（如平滑墙板）以及楼层间/内布局相似导致的感知混淆，该场景下的定位极具挑战性。本文聚焦于仅使用激光雷达数据，基于高精度建筑网格对机器人进行全局重定位。我们利用真实大规模网格中模拟生成的合成激光点云训练神经网络，采用带有PointNet++主干的扩散模型，可从单帧点云中建模多个可能的位置候选。该模型在受限且复杂的工地环境中仍能有效预测全局位置，学习到的位置分布具有多模态特性。我们在五个真实数据集上进行评估，平均定位准确率达77%（误差±2米），且均方误差较基线方法提升两倍。
原文摘要 · Abstract (English)
Mobile robots on construction sites require accurate pose estimation to perform autonomous surveying and inspection missions. Localization in construction sites is a particularly challenging problem due to the presence of repetitive features such as flat plastered walls and perceptual aliasing due to apartments with similar layouts inter and intra floors. In this paper, we focus on the global re-positioning of a robot with respect to an accurate scanned mesh of the building solely using LiDAR data. In our approach, a neural network is trained on synthetic LiDAR point clouds generated by simulating a LiDAR in an accurate real-life large-scale mesh. We train a diffusion model with a PointNet++ backbone, which allows us to model multiple position candidates from a single LiDAR point cloud. The resulting model can successfully predict the global position of LiDAR in confined and complex sites despite the adverse effects of perceptual aliasing. The learned distribution of potential global positions can provide multi-modal position distribution. We evaluate our approach across five real-world datasets and show the place recognition accuracy of 77% +/-2m on average while outperforming baselines at a factor of 2 in mean error.
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