Improving Extrinsics between RADAR and LIDAR using Learning
Peng Jiang Srikanth Saripalli
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.