Self-Positioning for Mobile Robot Indoor Navigation Based on Wheel Odometry, Inertia Measurement Unit and Ultra Wideband

2021 5th International Conference on Vision, Image and Signal Processing (ICVISP) Pub Date : 2021-12-01 DOI:10.1109/ICVISP54630.2021.00027

Shuliang Zhang, Xiangquan Tan, Qingwen Wu

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引用次数: 2

Abstract

GPS signals are often unavailable in indoor scenes where mobile robots often perform tasks. Without the help of global positioning signals, the self-positioning of indoor mobile robots becomes very difficult. In this paper, a self-positioning method for indoor mobile robots is proposed, which combines wheel odometer, inertial navigation unit (IMU) and ultrawideband (UWB). Firstly, through the analysis of each sensor participating in the fusion positioning, the positioning model of each sensor is determined. Secondly, the multi-sensor data fusion method based on extended Kalman filter is proposed so as to improve the overall positioning accuracy and robustness. Besides, in the indoor experimental environment, the feasibility of this method is verified by experiments on mobile robots. Finally, the experimental results show that, compared with the odometer and UWB positioning methods, the proposed method not only improves the positioning accuracy significantly, but also reduces the motion noise in pose estimation.

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基于车轮里程计、惯性测量单元和超宽带的移动机器人室内导航自定位

在移动机器人经常执行任务的室内场景中，GPS信号通常不可用。没有全球定位信号的帮助，室内移动机器人的自我定位变得非常困难。提出了一种结合车轮里程计、惯性导航单元(IMU)和超宽带(UWB)的室内移动机器人自定位方法。首先，通过对参与融合定位的各传感器的分析，确定各传感器的定位模型;其次，提出了基于扩展卡尔曼滤波的多传感器数据融合方法，提高了整体定位精度和鲁棒性;此外，在室内实验环境下，通过移动机器人的实验验证了该方法的可行性。最后，实验结果表明，与里程表和超宽带定位方法相比，该方法不仅显著提高了定位精度，而且降低了姿态估计中的运动噪声。

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2021 5th International Conference on Vision, Image and Signal Processing (ICVISP)

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