A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM
2026-07-17 • Robotics
Robotics
AI summaryⓘ
The authors rewrote the NeoSLAM algorithm using modern tools to make it faster and able to run in real time without losing much input data. They compared their new version with the older NeoSLAM and another algorithm called RatSLAM using different datasets and environments. Their tests showed that the new NeoSLAM processes data more quickly and creates maps that are about as accurate as RatSLAM's. This suggests their updated version is practical for real-time mapping tasks.
NeoSLAMRatSLAMSLAM algorithmsmodular architecturereal-time executionmap reconstructiontrajectory reconstructionROS2environmental datasetsprocessing throughput
Authors
Joao Victor T. Borges, Fabio Coelho, Paulo Padrao, Jose Fuentes, Ramon R. Costa, Liu Hsu, Leonardo Bobadilla
Abstract
This paper presents a new implementation of the NeoSLAM algorithm. The proposed version is a complete rewrite of NeoSLAM into a modular architecture using modern frameworks that, together, enable real-time execution with minimal discarding of input data. This work also provides a comparative evaluation between NeoSLAM and RatSLAM across three datasets under varying environmental conditions. The experimental results highlight differences in mapping consistency and trajectory reconstruction, demonstrating the effectiveness and practical applicability of the proposed ROS2-based implementation. The results indicate that the new NeoSLAM outperforms the original in terms of processing throughput for real-time applications and achieves comparable performance to RatSLAM in terms of map reconstruction across the evaluated datasets.