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
This work addresses perception challenges for industrial mobile manipulators, representing an incremental improvement with specific gains in accuracy.
The paper tackles robust perception for mobile robots in dynamic production environments by proposing a LiDAR framework with synthetic data training and multi-object tracking, achieving 83.12% Intersection over Union for pose estimation and 91.12% Higher Order Tracking Accuracy.
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.