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An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments

机译:在陌生环境中定位移动机器人的可扩展定位系统

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摘要

In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m.
机译:本文提出了一种可扩展的移动机器人定位系统。该系统包括一个立体摄像机模块,惯性测量单元(IMU)和一个超宽带(UWB)网络,该网络包括五个锚点,其中一个锚点的位置未知。定位系统中的锚不需要UWB锚之间的通信,也不需要锚的时钟同步。通过使用原始系统定位移动机器人,然后使用移动机器人与新锚点之间的距离估计新锚点的位置,可以在将新锚点添加到原始系统中后扩展系统。在不熟悉的环境(例如火灾和其他救援地点)中,它可以在扩展自身之后定位移动机器人。为了将新锚点添加到定位系统中,使用递归最小二乘(RLS)方法来估计新锚点的位置。基于最大熵准则(MCC)的最大熵卡尔曼滤波器(MCKF)用于融合来自UWB网络和IMU的数据。通过比较分别由视觉同时定位和制图(SLAM)系统和UWB系统给出的位置矢量,可以计算出移动机器人相对于导航框架的初始姿态。如实验部分所示,所提出的定位系统对所有锚点的定位结果的均方根误差(RMSE)为0.130 m。在陌生的环境中,RMSE为0.131 m,与原始系统给出的RMSE(0.137 m)接近,相差0.006 m。此外,基于新锚的欧拉距离的RMSE为0.061 m。

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