SPECal: Spatial Calibration Based on Self-Pose-Estimation Between mmWave Radar and Camera

Spatial calibration aligns heterogeneous sensor data, e.g., millimeter-wave radar (mmWave radar) and camera, in a common coordinate system, but cross-modal correspondence remains challenging due to differing data representations. In this paper, we propose SPECal to perform spatial calibration between mmWave radars and cameras mounted on a moving platform. Our core idea is to leverage the moving platform as a bridge by separately estimating the transformation matrices of the radar and the camera relative to the platform, thereby constructing their mutual mapping. Specifically, to mitigate dynamic interference, we treat dynamic points as outliers and apply a RANSAC-based method combined with two strategies: radar pose consistency filtering and inlier persistence weighting. To estimate the radar's pose from the radar point cloud, we also introduce a velocity projection model, where the radar-measured velocity is the projection of the actual velocity along the radar's radial direction. Furthermore, we propose a cross-modal spatial alignment method based on camera depth maps to refine the estimated pose. Experiment results demonstrate that SPECal achieves an average rotation error (RE) of 4.38° with a standard deviation of 1.19°, and an average translation error (TE) of 14.34 mm with a standard deviation of 3.59 mm.