Improving Extrinsics between RADAR and LIDAR using Learning
This addresses a crucial sensor fusion problem for autonomous driving systems, but it appears incremental as it builds on existing calibration techniques with a learning-based enhancement.
The paper tackles the problem of extrinsic calibration between RADAR and LIDAR sensors in autonomous driving, which is challenging due to RADAR's low accuracy and sparse data, and presents a method using simple targets and a one-step optimization with an MLP to minimize reprojection error, achieving improved calibration parameters as validated by experiments with Ouster-128 LIDAR and Navtech RADAR.
LIDAR and RADAR are two commonly used sensors in autonomous driving systems. The extrinsic calibration between the two is crucial for effective sensor fusion. The challenge arises due to the low accuracy and sparse information in RADAR measurements. This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems. The method employs simple targets to generate data, including correspondence registration and a one-step optimization algorithm. The optimization aims to minimize the reprojection error while utilizing a small multi-layer perception (MLP) to perform regression on the return energy of the sensor around the targets. The proposed approach uses a deep learning framework such as PyTorch and can be optimized through gradient descent. The experiment uses a 360-degree Ouster-128 LIDAR and a 360-degree Navtech RADAR, providing raw measurements. The results validate the effectiveness of the proposed method in achieving improved estimates of extrinsic calibration parameters.