Improving Extrinsics between RADAR and LIDAR using Learning

TL;DR

This paper introduces a deep learning-based method for improving the extrinsic calibration between RADAR and LIDAR sensors in autonomous vehicles, addressing challenges posed by RADAR's low accuracy and sparse data.

Contribution

It proposes a novel calibration approach using simple targets, correspondence registration, and a deep MLP regression within a gradient descent framework.

Findings

Enhanced calibration accuracy demonstrated on real sensor data

Effective use of deep learning for sensor fusion calibration

Validated improvements in extrinsic parameter estimation

Abstract

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.