Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain
Tim Hansen, Arturo Gomez-Chavez, Ilya Shimchik, Andreas Birk
TLDR
This paper proposes processing radar data in the frequency domain for robust multi-object tracking and odometry on fast-moving vehicles.
Key contributions
- Proposes processing radar data in the frequency domain for enhanced robustness.
- Improves robustness against noise and structural errors, outperforming feature-based methods.
- Frequency domain processing reveals all moving structures using correlation-based methods.
- Validates radar-only-odometry (FS2D) on fast vehicles using the Boreas dataset.
Why it matters
This paper addresses the challenge of robust multi-object tracking and odometry for fast-moving vehicles using radar. By leveraging frequency domain processing, it offers a more resilient approach than current methods, crucial for autonomous driving in dynamic environments.
Original Abstract
We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.
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