Entropy-Based $Sim(3)$ Calibration of 2D Lidars to Egomotion Sensors

TL;DR

This paper introduces an entropy-based method for calibrating 2D lidars to egomotion sensors, accurately recovering $Sim(3)$ transformations without scene constraints, demonstrated through simulations and real data.

Contribution

It extends existing methods to recover $Sim(3)$ transformations between lidar and monocular camera without scene constraints or fiducials.

Findings

Achieves millimetre-scale and sub-degree accuracy.

Robust across various environments and sensor rigs.

Does not require specific scene geometry or overlapping fields.

Abstract

This paper explores the use of an entropy-based technique for point cloud reconstruction with the goal of calibrating a lidar to a sensor capable of providing egomotion information. We extend recent work in this area to the problem of recovering the $Sim(3)$ transformation between a 2D lidar and a rigidly attached monocular camera, where the scale of the camera trajectory is not known a priori. We demonstrate the robustness of our approach on realistic simulations in multiple environments, as well as on data collected from a hand-held sensor rig. Given a non-degenerate trajectory and a sufficient number of lidar measurements, our calibration procedure achieves millimetre-scale and sub-degree accuracy. Moreover, our method relaxes the need for specific scene geometry, fiducial markers, or overlapping sensor fields of view, which had previously limited similar techniques.