Calibrating LiDAR and Camera using Semantic Mutual information
This addresses calibration challenges in multi-sensor systems for robotics and autonomous vehicles, though it appears incremental as it builds on existing mutual information methods with semantic enhancements.
The paper tackles the problem of automatic, targetless extrinsic calibration between LiDAR and camera sensors by maximizing semantic mutual information, achieving improved accuracy on benchmark datasets like KITTI360 and RELLIS-3D compared to recent approaches.
We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural network to estimate semantic mutual information, and matrix exponential for calibration computation. Using kernel-based sampling to sample data from camera measurement based on LiDAR projected points, we formulate the problem as a novel differentiable objective function which supports the use of gradient-based optimization methods. We also introduce an initial calibration method using 2D MI-based image registration. Finally, we demonstrate the robustness of our method and quantitatively analyze the accuracy on a synthetic dataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D benchmark datasets, showing improvement over recent comparable approaches.