Loss Function Regularization on the Iterated Racing Procedure for Automatic Tuning of RatSLAM Parameters

Gomes, Paulo Gabriel Borralho; Oliveira, Cicero Joe Rafael Lima de; Menezes, Matheus Chaves; Ribeiro, Paulo Rogério de Almeida; Oliveira, Alexandre César Muniz de

doi:10.1007/978-3-031-08443-0_4

Cited by 1 publication

(1 citation statement)

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“…In the accuracy and performance contexts (item (i)), xRatSLAM has already been used by researchers in experiments that require shorter execution times to perform time-consuming tasks, such as parameter tuning in long-term mapping [ 30 , 31 ]. However, opportunities for enhancements are highlighted below to show how the framework can be improved: Other RatSLAM module implementations; A built-in assessment module for mapping accuracy evaluation; Dynamic libraries (plug-ins) in the module inclusion mechanism; Support for 3D SLAM, as required for unmanned aerial vehicles (UAV: drones) or uncrewed underwater vehicles (UUV); A ROS wrapper for xRatSLAM; An interface for other programming languages such as Python; A module repository for sharing implementations between different users; Usability improvements to suit neuroscience theorists and practitioners.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

xRatSLAM: An Extensible RatSLAM Computational Framework

Muñoz

Menezes

Freitas

et al. 2022

Sensors

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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.

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