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

TL;DR

This paper presents a ROS 2-based LiDAR perception framework for mobile robots in dynamic environments, combining synthetic data, equivariant 3D detection, and multi-object tracking to improve robustness and accuracy.

Contribution

It introduces a novel LiDAR perception framework that integrates synthetic data training, transformation-equivariant detection, and multi-object tracking for industrial robots.

Findings

Achieved 62.6% IoU for pose estimation, improved to 83.12% with tracking.

Reached 91.12% Higher Order Tracking Accuracy.

Validated across 72 scenarios with motion capture technology.

Abstract

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.