Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing 210096, China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2019 Sep 18;19(18):4025. doi: 10.3390/s19184025.
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.
本文提出了一种可扩展的移动机器人定位系统。该系统包括立体相机模块、惯性测量单元（IMU）和超宽带（UWB）网络，其中包括五个锚点，其中一个的位置未知。定位系统中的锚点不需要 UWB 锚点之间的通信，也不需要锚点的时钟同步。通过使用原始系统定位移动机器人，然后使用移动机器人和新锚点之间的测距来估计新锚点的位置，可以在向原始系统添加新锚点后扩展系统。在不熟悉的环境（如火灾和其他救援地点）中，系统可以在扩展自身后定位移动机器人。要将新锚点添加到定位系统中，使用递归最小二乘（RLS）方法估计新锚点的位置。基于最大相关熵准则（MCC）的最大相关熵卡尔曼滤波器（MCKF）用于融合来自 UWB 网络和 IMU 的数据。通过比较视觉同时定位和制图（SLAM）系统和 UWB 系统分别给出的位置向量，计算移动机器人相对于导航帧的初始姿态。如实验部分所示，使用所有锚点的提出的定位系统的定位结果的均方根误差（RMSE）为 0.130m。在不熟悉的环境中，RMSE 为 0.131m，与原始系统的 RMSE（0.137m）相差 0.006m。此外，新锚点的基于欧拉距离的 RMSE 为 0.061m。
