Abstract
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.
Highlights
The indoor positioning system (IPS) has been widely applied in many fields
Its positioning principle can be divided into time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA)
In order to solve the problem of the non-Gaussian signal model, Chen and Liu propose a filtering algorithm based on the maximum correntropy criterion (MCC) and called the maximum correntropy Kalman filter (MCKF) [37,38,39]
Summary
The indoor positioning system (IPS) has been widely applied in many fields. Mobile robots, mine workers, indoor parking lots, factory cargo automation management, fire rescue, location detection of soldiers and so on rely on the support of indoor positioning technology. In order to locate indoor mobile robots, scholars have proposed many methods, including UWB positioning, inertial measurement unit (IMU) based inertial navigation positioning, vision-based SLAM positioning and multi-sensor fusion based combination positioning [16,17,18,19,20,21].
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