PeLiCal: Targetless Extrinsic Calibration via Penetrating Lines for RGB-D Cameras with Limited Co-visibility

TL;DR

PeLiCal is a real-time, targetless extrinsic calibration method for RGB-D camera systems with limited overlap, utilizing line features and a novel voting algorithm for outlier robustness.

Contribution

It introduces a line-based calibration approach that does not require targets and is robust to outliers, suitable for cameras with limited field of view overlap.

Findings

Achieves real-time calibration performance.

Effectively filters out outliers with a new convergence voting algorithm.

Demonstrates robustness and accuracy in limited-overlap scenarios.

Abstract

RGB-D cameras are crucial in robotic perception, given their ability to produce images augmented with depth data. However, their limited FOV often requires multiple cameras to cover a broader area. In multi-camera RGB-D setups, the goal is typically to reduce camera overlap, optimizing spatial coverage with as few cameras as possible. The extrinsic calibration of these systems introduces additional complexities. Existing methods for extrinsic calibration either necessitate specific tools or highly depend on the accuracy of camera motion estimation. To address these issues, we present PeLiCal, a novel line-based calibration approach for RGB-D camera systems exhibiting limited overlap. Our method leverages long line features from surroundings, and filters out outliers with a novel convergence voting algorithm, achieving targetless, real-time, and outlier-robust performance compared to existing methods. We open source our implementation on https://github.com/joomeok/PeLiCal.git.