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.

RGB-D 相机在机器人感知中至关重要，由于其能够生成带有深度数据的图像。然而，它们的有限视野经常需要多个相机来覆盖更大的区域。在多相机 RGB-D 设置中，目标通常是减少相机重叠，以尽可能少的相机进行空间覆盖。这篇研究提出了一种新的基于线条的 RGB-D 相机系统校准方法，通过利用周围的长线特征，并使用一种新的收敛投票算法来滤除异常值，相对于现有方法实现了无需目标、实时和鲁棒性能。我们在 https://github.com/joomeok/PeLiCal.git 上开源了我们的实现。