Adaptive robots in dynamic production environments require robust perception capabilities, including 6D pose estimation and multi-object tracking. To address limitations in real-world data dependency, noise robustness, and spatiotemporal consistency, a LiDAR framework based on the Robot Operating System integrating a synthetic-data-trained Transformation-Equivariant 3D Detection with multi-object-tracking leveraging center poses is proposed. Validated across 72 scenarios with motion capture technology, overall results yield an Intersection over Union of 62.6% for standalone pose estimation, rising to 83.12% with multi-object-tracking integration. Our LiDAR-based framework achieves 91.12% of Higher Order Tracking Accuracy, advancing robustness and versatility of LiDAR-based perception systems for industrial mobile manipulators.

翻译：动态生产环境中的自适应机器人需要具备鲁棒的感知能力，包括6D位姿估计与多目标跟踪。为解决真实数据依赖、噪声鲁棒性及时空一致性等方面的局限性，本文提出一种基于机器人操作系统的激光雷达框架，该框架集成经合成数据训练的变换等变3D检测模块，并利用中心位姿实现多目标跟踪。通过动作捕捉技术在72个场景中验证，独立位姿估计的交并比为62.6%，而集成多目标跟踪后提升至83.12%。基于激光雷达的框架实现了91.12%的高阶跟踪准确率，推动了工业移动机械臂激光雷达感知系统的鲁棒性与通用性。