xRatSLAM: An Extensible RatSLAM Computational Framework

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 21; p. 8305
• Main Authors: de Souza Muñoz, Mauro Enrique, Chaves Menezes, Matheus, Pignaton de Freitas, Edison, Cheng, Sen, de Almeida Ribeiro, Paulo Rogério, de Almeida Neto, Areolino, Muniz de Oliveira, Alexandre César
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.10.2022; MDPI
• Subjects: Algorithms; Augmented reality; Cameras; Comparative analysis; Competition; Design; Electronics in navigation; image SLAM; Libraries; Localization; RatSLAM; Robotics; Robots; Sensors; simultaneous localization and mapping
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22218305

Simultaneous localization and mapping (SLAM) refers to techniques for autonomously constructing a map of an unknown environment while, at the same time, locating the robot in this map. RatSLAM, a prevalent method, is based on the navigation system found in rodent brains. It has served as a base algorithm for other bioinspired approaches, and its implementation has been extended to incorporate new features. This work proposes xRatSLAM: an extensible, parallel, open-source framework applicable for developing and testing new RatSLAM variations. Tests were carried out to evaluate and validate the proposed framework, allowing the comparison of xRatSLAM with OpenRatSLAM and assessing the impact of replacing framework components. The results provide evidence that the maps produced by xRatSLAM are similar to those produced by OpenRatSLAM when they are fed with the same input parameters, which is a positive result, and that implemented modules can be easily changed without impacting other parts of the framework.