LAUSR

The strategy of fusing multi-model data especially from cameras, light detection and ranging sensors (LiDAR), is frequently considered in robotics to enhance the performance of the perception and navigation tasks. Extrinsic calibration, which spatially aligns different sources into a unified coordinate representation, directly determines the performance of the combined data. In this letter, we propose PBACalib, a novel targetless extrinsic calibration algorithm aiming at the dense LiDAR-camera system based on the plane-constrained bundle adjustment (PBA). The proposed method utilizes the feature points derived from a prominent plane in the scene and iteratively minimizes the reprojection error. A maximum likelihood estimator (MLE) is designed by considering the uncertainty information of the measurements. Furthermore, we explore the distribution of collected data and characterize the robustness and solvability of the extrinsic estimates using a confidence factor. Simulation and real-world experiments both qualitatively and quantitatively demonstrate the robustness and accuracy of our method. The comparison experiments show that the proposed method outperforms another targetless method. To benefit the community, Matlab code has been publicly released on Github.