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

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• The proposed ROS 2-based LiDAR perception framework achieves an Intersection over Union of 62.6% for standalone 6D pose estimation.result 95%
• Integration of multi-object tracking improves pose estimation performance to 83.12% Intersection over Union.result 95%
• The framework achieves 91.12% Higher Order Tracking Accuracy for LiDAR-based perception.result 95%
• The framework utilizes synthetic data generation to address limitations in real-world data dependency.method 90%
• The framework employs Transformation-Equivariant 3D Detection for improved pose estimation.technical 90%
• Multi-object tracking in the framework leverages center poses for improved performance.technical 90%
• The framework was validated across 72 scenarios using motion capture technology.method 95%
• The framework advances robustness and versatility of LiDAR-based perception systems for industrial mobile manipulators.technical 85%

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