Robotics Institute, Beihang University, Beijing 100191, China.
Sensors (Basel). 2018 Sep 14;18(9):3097. doi: 10.3390/s18093097.
This paper simultaneously calibrates odometry parameters and the relative pose between a monocular camera and a robot automatically. Most camera pose estimation methods use natural features or artificial landmark tools. However, there are mismatches and scale ambiguity for natural features; the large-scale precision landmark tool is also challenging to make. To solve these problems, we propose an automatic process to combine multiple composite targets, select keyframes, and estimate keyframe poses. The composite target consists of an aruco marker and a checkerboard pattern. First, an analytical method is applied to obtain initial values of all calibration parameters; prior knowledge of the calibration parameters is not required. Then, two optimization steps are used to refine the calibration parameters. Planar motion constraints of the camera are introduced in these optimizations. The proposed solution is automatic; manual selection of keyframes, initial values, and robot construction within a specific trajectory are not required. The competing accuracy and stability of the proposed method under different target placements and robot paths are tested experimentally. Positive effects on calibration accuracy and stability are obtained when (1) composite targets are adopted; (2) two optimization steps are used; (3) plane motion constraints are introduced; and (4) target numbers are increased.
本文同时自动校准了单目相机和机器人之间的里程计参数和相对姿态。大多数相机位姿估计方法使用自然特征或人工地标工具。然而，自然特征存在不匹配和尺度模糊问题；大尺寸高精度地标工具也难以制作。为了解决这些问题，我们提出了一种自动组合多个复合目标、选择关键帧并估计关键帧姿态的方法。复合目标由 aruco 标记和棋盘格图案组成。首先，应用解析方法获得所有校准参数的初始值；不需要校准参数的先验知识。然后，使用两个优化步骤来细化校准参数。在这些优化中引入了相机的平面运动约束。所提出的解决方案是自动的；不需要手动选择关键帧、初始值和在特定轨迹内的机器人构建。实验测试了不同目标位置和机器人路径下，所提出方法的竞争准确性和稳定性。当采用（1）复合目标、（2）使用两个优化步骤、（3）引入平面运动约束和（4）增加目标数量时，对校准准确性和稳定性有积极影响。
