Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles

TL;DR

This paper introduces a motion guided approach for self-calibration of LiDAR and camera systems and a real-time depth upsampling method, enhancing autonomous vehicle perception with improved accuracy and efficiency.

Contribution

It presents a novel target-less self-calibration technique and an efficient depth upsampling formulation tailored for real-time autonomous vehicle applications.

Findings

Effective calibration accuracy demonstrated on urban data

Real-time depth upsampling improves perception quality

Suitable for mobile robotics and autonomous vehicles

Abstract

This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling. The calibration parameters are estimated by optimizing an objective function that penalizes distances between 2D and re-projected 3D motion vectors obtained from time-synchronized image and point cloud sequences. For upsampling, a simple, yet effective and time efficient formulation that minimizes depth gradients subject to an equality constraint involving the LiDAR measurements is proposed. Validation is performed on recorded real data from urban environments and demonstrations that our two methods are effective and suitable to mobile robotics and autonomous vehicle applications imposing real-time requirements is shown.