基于合成数据的激光雷达框架，提升动态工厂中机器人的感知鲁棒性。
ROS 2-Based LiDAR Perception Framework for Mobile Robots in Dynamic Production Environments, Utilizing Synthetic Data Generation, Transformation-Equivariant 3D Detection and Multi-Object Tracking
- 用合成数据训练3D检测模型，实现变换等变性以应对环境变化。
- 独立姿态估计IoU达62.6%，融合多目标跟踪后升至83.12%。
- 适合工业移动操作机器人，提升复杂动态场景下的感知性能。
动态生产环境中自适应机器人需要具备6D位姿估计与多目标跟踪的鲁棒感知能力。为解决真实数据依赖、噪声鲁棒性及时空一致性问题，提出一种基于机器人操作系统（ROS 2）的激光雷达感知框架，集成基于合成数据训练的变换等变3D检测模块，并结合基于中心位姿的多目标跟踪算法。在72个场景下通过运动捕捉技术验证，独立姿态估计的交并比（IoU）达62.6%，融合多目标跟踪后提升至83.12%。该框架在高阶跟踪准确率上达到91.12%，显著增强了工业级移动操作机器人激光雷达感知系统的鲁棒性与通用性。
原文摘要 · Abstract (English)
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.
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