Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds

Matthias Zeller

doi:10.1109/TRO.2023.3338972

Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds

2024-01-01

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AI

DOI：10.1109/TRO.2023.3338972

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The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this article, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our radar instance transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation.

Keywords:

Radar

Point cloud compression

Feature extraction

Transformers

Task analysis

Encoding

Sensors

Deep learning in robotics and automation

object detection

radar perception

segmentation and categorization

semantic scene understanding

Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds

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