复杂环境下室内移动机器人的视觉定位策略

Xiaohan Lei, Fei Zhang, Junyi Zhou, Weiwei Shang
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引用次数: 2

摘要

基于视觉的移动定位技术具有广阔的应用前景。但易受外界环境因素干扰,在复杂环境下定位精度和鲁棒性较差。为此,本文设计了一种融合立体视觉里程计和惯性测量单元(IMU)数据的复杂环境下高精度视觉定位策略。采用多传感器标定方法补偿IMU的测量误差和立体摄像机的参数误差。为了实现多传感器数据的同步采集与处理,设计了一种基于时间戳的多传感器数据同步对准方法。在Unscented卡尔曼滤波(UKF)算法的基础上,实现了一种非线性数据耦合方法,将立体视觉测程和IMU信息融合在一起,实现高精度定位。在复杂开放的实验室环境中,融合定位的实验结果表明,移动机器人定位的精度和鲁棒性得到了显著提高。全局最大误差减小15%,方差减小5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Localization Strategy for Indoor Mobile Robots in the Complex Environment
Vision-based mobile positioning technology has a broad application prospect. Still, it is easy to be disturbed by external environmental factors, and the positioning accuracy and robustness in a complex environment are poor. Therefore, this paper designs a high-precision visual positioning strategy for a complex environment via fusing stereo visual odometry and Inertial Measurement Unit (IMU) data. A multi-sensor calibration method is utilized to compensate for the measurement error of IMU and the parameter error of the stereo camera. A multi-sensor data synchronization alignment method based on timestamp is also designed to realize the synchronous acquisition and processing of multi-sensor data. Based on the Unscented Kalman Filter (UKF) algorithm, we implement a nonlinear data coupling method to fuse the stereo visual odometry and IMU information to obtain high-precision positioning. In the complex and open laboratory environment, the experimental results of fused localization show that the accuracy and robustness of the mobile robot localization are significantly improved. The global maximum error is reduced by 15%, and the variance is reduced by 5%.
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