TRLO：一种具有3D动态目标跟踪与移除功能的高效LiDAR里程计
TRLO: An Efficient LiDAR Odometry with 3D Dynamic Object Tracking and Removal
AI总结：
本文提出TRLO动态LiDAR里程计，结合深度学习检测、UKF多目标跟踪与哈希关键帧管理，在动态城市场景中提升定位精度并生成更干净的地图。
摘要：
同步状态估计与建图是移动机器人在动态城市场景中工作的核心能力。现有大多数SLAM方案严重依赖以静态为主的假设。然而，由于存在移动车辆和行人，这一假设并不总是成立，导致定位精度下降和地图失真。为应对这一挑战，我们提出TRLO，一种动态LiDAR里程计，可高效提升状态估计精度并生成更干净的点云地图。为高效检测周围环境中的动态目标，采用基于深度学习的方法生成检测边界框。随后，我们设计了一种基于无迹卡尔曼滤波（UKF）和最近邻（NN）策略的3D多目标跟踪器，以可靠地识别并移除动态目标。接着，采用快速两阶段迭代最近点求解器，利用清理后的静态点云进行状态估计。值得注意的是，我们提出了一种新颖的基于哈希的关键帧数据库管理方法，用于快速访问和检索关键帧。此外，利用所有检测到的目标边界框施加位姿一致性约束，以进一步优化最终状态估计。在KITTI和UrbanLoco数据集上进行的大量评估和消融研究表明，与基线方法相比，我们的方法不仅实现了更精确的状态估计，还生成了更干净的地图。
英文摘要：
Simultaneous state estimation and mapping is an essential capability for mobile robots working in dynamic urban environment. The majority of existing SLAM solutions heavily rely on a primarily static assumption. However, due to the presence of moving vehicles and pedestrians, this assumption does not always hold, leading to localization accuracy decreased and maps distorted. To address this challenge, we propose TRLO, a dynamic LiDAR odometry that efficiently improves the accuracy of state estimation and generates a cleaner point cloud map. To efficiently detect dynamic objects in the surrounding environment, a deep learning-based method is applied, generating detection bounding boxes. We then design a 3D multi-object tracker based on Unscented Kalman Filter (UKF) and nearest neighbor (NN) strategy to reliably identify and remove dynamic objects. Subsequently, a fast two-stage iterative nearest point solver is employed to solve the state estimation using cleaned static point cloud. Note that a novel hash-based keyframe database management is proposed for fast access to search keyframes. Furthermore, all the detected object bounding boxes are leveraged to impose posture consistency constraint to further refine the final state estimation. Extensive evaluations and ablation studies conducted on the KITTI and UrbanLoco datasets demonstrate that our approach not only achieves more accurate state estimation but also generates cleaner maps, compared with baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
