Open Access

ARTICLE

Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

by Gengyu Ge^1,2,*, Zhong Qin¹, Xin Chen¹

1 School of Information Engineering, Zunyi Normal University, Zunyi, 563006, China
2 School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

* Corresponding Author: Gengyu Ge. Email:

Computers, Materials & Continua 2023, 74(3), 6351-6369. https://doi.org/10.32604/cmc.2023.035832

Received 06 September 2022; Accepted 27 October 2022; Issue published 28 December 2022

Abstract

Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses a 2D laser Lidar to build an occupancy grid map, then locates the robot according to the known grid map. Unfortunately, this scheme only works effectively in those areas with salient geometrical features. The traditional particle filter always fails for areas with similar structures, such as a long corridor. To solve their shortcomings, this paper proposes a novel coarse-to-fine paradigm that uses WSN to assist mobile robot localization in a geometrically similar environment. Firstly, the fingerprints database is built in the offline stage to get reference distance information. The distance data is determined by the statistical mean value of multiple RSSI values. Secondly, a hybrid map with grid cells and RSSI values is constructed when the mobile robot moves from a starting point to the ending place. Thirdly, the RSSI values are thought of as a basic reference to get a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results demonstrate that our approach is effective and robust for global localization. The localization success rate reaches 97.0% and the average moving distance is only 0.74 meters, while the traditional method always fails. In addition, the method also works well when the mobile robot is kidnapped to another position in the environment.

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Keywords

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