Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
Boreas: A Multi-Season Autonomous Driving Dataset , url =
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The paper recasts Gaussian-process continuous-time estimation in factor-graph language and supplies three GTSAM implementations to lower the barrier to adoption.
Two radar odometry baselines improve trajectory estimates on challenging off-road routes in the GO dataset.
Frequency-domain radar processing with Fourier SOFT in 2D improves robustness for radar-only odometry and multi-object tracking in high-dynamic scenes compared with feature-based methods.
citing papers explorer
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Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
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Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
The paper recasts Gaussian-process continuous-time estimation in factor-graph language and supplies three GTSAM implementations to lower the barrier to adoption.
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Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
Two radar odometry baselines improve trajectory estimates on challenging off-road routes in the GO dataset.
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Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain
Frequency-domain radar processing with Fourier SOFT in 2D improves robustness for radar-only odometry and multi-object tracking in high-dynamic scenes compared with feature-based methods.