Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain
Authors: Tim Hansen, Arturo Gomez-Chavez, Ilya Shimchik, Andreas Birk
Organizations: School of Computer Science and Engineering at Constructor University Bremen, Germany. · Constructor Knowledge Labs (CKL), Bremen, Germany.
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.
Figures & tables
Fig. 1 : Registration in the frequency domain, here of sonar data with FS2D, can not only deal very well with noisy data. There is also the so far neglected advantage that the underlying processing with correlation methods leads to peaks that indicate the transformation parameters for multiple moving structures in the data in parallel.
Fig. 2 : The Constructor University (CU) vehicle used for high-velocity autonomous driving.
Fig. 3 : The A2RL races feature fully autonomous driving at high velocities on the Yas Marina Formula-1 race-track in Abu Dhabi.
Fig. 4 : Autonomous overtaking includes the perception challenge of Multi-Object-Tracking (MOT). For planning and executing the maneuver, the vehicle A itself, the car B that is to be overtaken, as well as an unknown number of vehicles Ci in the vicinity ( i≥0 ) have to be tracked.
Fig. 5 : The Glen Shield route from the Boreas dataset (image from the Boreas website https://www.boreas.utias.utoronto.ca/ using OpenStreetMap).
Fig. 6 : The estimated trajectories (blue) and the ground truth (red) for the first two datasets as illustrative examples. Note that the estimated trajectories are purely based on pairwise registration of radar scans without any other localization or motion estimation, and that they include all outliers.
Fig. 7 : Three examples of the FS2D registration. On the top, an overlay of two scans in red and blue is shown using their ground truth poses. The actual motion is hence represented in the translation and rotation of the blue scan relative to the red one. Below, the same scan pairs are projected according to the motion estimates from FS2D, which are very close to the ground truth.