xRatSLAM: An Extensible RatSLAM Computational Framework

Abstract

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.

Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory

“…In the secondary school curriculums, students may have learned about programming algorithms (e.g., the recursive and backtracking algorithms), natural language recognition algorithms (e.g., decision tree and logistic regression), and simultaneous localization and mapping (SLAM) algorithms [4,23]. SLAM refers to a technology for autonomously constructing a map of an unknown environment while positioning the robot on this map [24]. The algorithm is a fundamental mechanism to tackle the positions autonomously and flexibly, and is available for application in different contexts, such as active cooperation and localizing each other, and cooperating in a 3D mapping service [25].…”