用运动数据简化多相机与机器人的标定，抗噪声且易用。
MEMROC: Multi-Eye to Mobile RObot Calibration
- 基于已知标定板和运动数据，减少所需图像数量。
- 通过地面检测实现完整的6自由度外参标定，精度更高。
- 适合频繁标定的动态环境，尤其适用于移动机器人日常运行。
本文提出MEMROC（Multi-Eye to Mobile RObot Calibration），一种新颖的基于运动的标定方法，可简化多相机相对于移动机器人参考系的精确标定过程。MEMROC利用已知标定板，在优化过程中仅需少量图像即可实现高精度标定。同时，它借助鲁棒的地面平面检测，实现完整的6-DoF外参标定，克服了现有许多方法难以估计完整相机位姿的关键缺陷。该方法解决了动态环境中因日常使用、操作调整或机器人运动振动导致摄像头轻微偏移或位置变化所引发的频繁重标定需求。MEMROC对噪声里程计数据具有显著鲁棒性，所需校准输入数据极少。在合成数据与真实数据上的全面实验表明，MEMROC在准确性、鲁棒性和易用性方面均优于现有最先进方法。为促进后续研究，代码已公开于 https://github.com/davidea97/MEMROC.git。
原文摘要 · Abstract (English)
This paper presents MEMROC (Multi-Eye to Mobile RObot Calibration), a novel motion-based calibration method that simplifies the process of accurately calibrating multiple cameras relative to a mobile robot's reference frame. MEMROC utilizes a known calibration pattern to facilitate accurate calibration with a lower number of images during the optimization process. Additionally, it leverages robust ground plane detection for comprehensive 6-DoF extrinsic calibration, overcoming a critical limitation of many existing methods that struggle to estimate the complete camera pose. The proposed method addresses the need for frequent recalibration in dynamic environments, where cameras may shift slightly or alter their positions due to daily usage, operational adjustments, or vibrations from mobile robot movements. MEMROC exhibits remarkable robustness to noisy odometry data, requiring minimal calibration input data. This combination makes it highly suitable for daily operations involving mobile robots. A comprehensive set of experiments on both synthetic and real data proves MEMROC's efficiency, surpassing existing state-of-the-art methods in terms of accuracy, robustness, and ease of use. To facilitate further research, we have made our code publicly available at https://github.com/davidea97/MEMROC.git.
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