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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| _version_ | 1826312602927497216 |
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| author | Fu, LFT Fallon, M |
| author_facet | Fu, LFT Fallon, M |
| author_sort | Fu, LFT |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-04-09T03:55:28Z |
| format | Conference item |
| id | oxford-uuid:dfa9a795-a49c-4b70-88a9-b1f14de342a7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:55:28Z |
| publishDate | 2023 |
| publisher | Journal of Machine Learning Research |
| record_format | dspace |
| spelling | oxford-uuid:dfa9a795-a49c-4b70-88a9-b1f14de342a72024-03-12T12:27:29ZBatch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibrationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:dfa9a795-a49c-4b70-88a9-b1f14de342a7EnglishSymplectic ElementsJournal of Machine Learning Research2023Fu, LFTFallon, MAccurate 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. |
| spellingShingle | Fu, LFT Fallon, M Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration |
| title | Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration |
| title_full | Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration |
| title_fullStr | Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration |
| title_full_unstemmed | Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration |
| title_short | Batch differentiable pose refinement for in-the-wild camera/LiDAR extrinsic calibration |
| title_sort | batch differentiable pose refinement for in the wild camera lidar extrinsic calibration |
| work_keys_str_mv | AT fulft batchdifferentiableposerefinementforinthewildcameralidarextrinsiccalibration AT fallonm batchdifferentiableposerefinementforinthewildcameralidarextrinsiccalibration |