Single Localization Method Research Articles

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Abstract Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

2D lidar and ultra-wideband fusion location algorithm based on landmark assistance

In an indoor environment where global positioning system (GPS) signals are severely attenuated, ultra-wideband (UWB) and 2D lidar are widely used in the autonomous positioning of mobile platforms. However, the presence of nonline-of-sight (NLOS) environments can lead to large errors in UWB positioning, and 2D lidar will increase the cumulative error due to the loss of accuracy in sparsely textured scenes. In order to reduce the positioning error, a UWB and 2D lidar fusion positioning algorithm based on the assistance of a few landmarks is proposed in this paper. Considering the colored noise of lidar location data, a Kalman filter algorithm based on cumulative error analysis is proposed. First, the lidar error curve is fitted by the least-square method, and then the relationship between the noise covariance matrix and the lidar cumulative error function is established by introducing the scale factor, which is substituted into the Kalman prediction equation. Experimental results show that the proposed multi-sensor fusion localization algorithm is feasible, and compared with the single localization method, the proposed fusion algorithm can significantly improve the localization accuracy; matching landmarks can achieve a positioning accuracy of 0.15 m, which is about 24.4% lower than the root mean square error of traditional Kalman filter.