Targetless 6DoF Calibration of LiDAR and 2D Scanning Radar Based on Cylindrical Occupancy

• URL: http://arxiv.org/abs/2503.17002v1
• Date: Fri, 21 Mar 2025 10:09:04 GMT
• Title: Targetless 6DoF Calibration of LiDAR and 2D Scanning Radar Based on Cylindrical Occupancy
• Authors: Weimin Wang, Yu Du, Ting Yang, Yu Liu,
• Abstract summary: LiRaCo is a targetless calibration approach for the extrinsic 6DoF calibration of LiDAR and Radar sensors.<n>LiRaCo leverages a spatial occupancy consistency between LiDAR point clouds and Radar scans in a common cylindrical representation.<n>A cost function involving extrinsic calibration parameters is formulated based on the spatial overlap of 3D grids and LiDAR points.
• Score: 8.895838973148452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Owing to the capability for reliable and all-weather long-range sensing, the fusion of LiDAR and Radar has been widely applied to autonomous vehicles for robust perception. In practical operation, well manually calibrated extrinsic parameters, which are crucial for the fusion of multi-modal sensors, may drift due to the vibration. To address this issue, we present a novel targetless calibration approach, termed LiRaCo, for the extrinsic 6DoF calibration of LiDAR and Radar sensors. Although both types of sensors can obtain geometric information, bridging the geometric correspondences between multi-modal data without any clues of explicit artificial markers is nontrivial, mainly due to the low vertical resolution of scanning Radar. To achieve the targetless calibration, LiRaCo leverages a spatial occupancy consistency between LiDAR point clouds and Radar scans in a common cylindrical representation, considering the increasing data sparsity with distance for both sensors. Specifically, LiRaCo expands the valid Radar scanned pixels into 3D occupancy grids to constrain LiDAR point clouds based on spatial consistency. Consequently, a cost function involving extrinsic calibration parameters is formulated based on the spatial overlap of 3D grids and LiDAR points. Extrinsic parameters are finally estimated by optimizing the cost function. Comprehensive quantitative and qualitative experiments on two real outdoor datasets with different LiDAR sensors demonstrate the feasibility and accuracy of the proposed method. The source code will be publicly available.

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