LACMOR, Federal University of Maranhão, Av. dos Portugueses, 1966, São Luís 65080-805, MA, Brazil.
INF, Federal University of Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre 91501-970, RS, Brazil.
Sensors (Basel). 2022 Oct 29;22(21):8305. 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.
同时定位与建图（SLAM）指的是自主构建未知环境地图，同时在该地图中定位机器人的技术。基于啮齿动物大脑导航系统的 RatSLAM 是一种流行方法，它已成为其他仿生方法的基础算法，并且其实现已扩展到包含新功能。这项工作提出了 xRatSLAM：一个可扩展的、并行的、开源框架，适用于开发和测试新的 RatSLAM 变体。进行了测试以评估和验证所提出的框架，允许将 xRatSLAM 与 OpenRatSLAM 进行比较，并评估替换框架组件的影响。结果表明，当使用相同的输入参数时，xRatSLAM 生成的地图与 OpenRatSLAM 生成的地图相似，这是一个积极的结果，并且可以轻松更改实现的模块，而不会影响框架的其他部分。
