Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration
Accurate camera to LiDAR (Light Detection and Ranging) extrinsic calibration is important for robotic tasks carrying out tight sensor fusion — such as target tracking and odometry. Calibration is typically performed before deployment in controlled conditions using calibration targets, however, this...
| Հիմնական հեղինակներ: | , |
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| Ձևաչափ: | Conference item |
| Լեզու: | English |
| Հրապարակվել է: |
Journal of Machine Learning Research
2023
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| Ամփոփում: | Accurate camera to LiDAR (Light Detection and Ranging) extrinsic calibration is important for robotic tasks carrying out tight sensor fusion — such as target tracking and odometry. Calibration is typically performed before deployment in controlled conditions using calibration targets, however, this limits scalability and subsequent recalibration. We propose a novel approach for target-free camera-LiDAR calibration using end-to-end direct alignment which doesn’t need calibration targets. Our batched formulation enhances sample efficiency during training and robustness at inference time. We present experimental results, on publicly available real-world data, demonstrating 1.6cm/0.07∘median accuracy when transferred to unseen sensors from held-out data sequences. We also show state-of-the-art zero-shot transfer to unseen cameras, LiDARs, and environments. |
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