DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow

TL;DR

DXQ-Net is an innovative end-to-end differentiable approach for LiDAR-camera extrinsic calibration that leverages a probabilistic model to improve accuracy and generalization, reducing manual effort and enhancing online calibration.

Contribution

The paper introduces DXQ-Net, a novel calibration method that incorporates a differentiable pose estimation and probabilistic modeling for improved accuracy and generalization.

Findings

Achieves competitive translation accuracy

State-of-the-art rotation calibration performance

Significantly better generalization than previous deep learning methods

Abstract

Accurate LiDAR-camera extrinsic calibration is a precondition for many multi-sensor systems in mobile robots. Most calibration methods rely on laborious manual operations and calibration targets. While working online, the calibration methods should be able to extract information from the environment to construct the cross-modal data association. Convolutional neural networks (CNNs) have powerful feature extraction ability and have been used for calibration. However, most of the past methods solve the extrinsic as a regression task, without considering the geometric constraints involved. In this paper, we propose a novel end-to-end extrinsic calibration method named DXQ-Net, using a differentiable pose estimation module for generalization. We formulate a probabilistic model for LiDAR-camera calibration flow, yielding a prediction of uncertainty to measure the quality of LiDAR-camera data association. Testing experiments illustrate that our method achieves a competitive with other methods for the translation component and state-of-the-art performance for the rotation component. Generalization experiments illustrate that the generalization performance of our method is significantly better than other deep learning-based methods.