A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM

Joao Victor T. Borges; Fabio Coelho; Paulo Padrao; Jose Fuentes; Ramon R. Costa; Liu Hsu; Leonardo Bobadilla

cs.ROJul 17, 2026

A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM

Authors: Joao Victor T. Borges, Fabio Coelho, Paulo Padrao, Jose Fuentes, Ramon R. Costa, Liu Hsu, Leonardo Bobadilla

Organizations: Department of Electrical Engineering, Federal University of Rio de Janeiro, Brazil · Department of Mathematics and Computer Science, Providence College, Providence, RI, USA · School of Computing and Information Sciences, Florida International University, Miami, FL, USA

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.

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Multi-Submap Implicit Neural SLAM with Local-to-Global Loop Closure for Large-Scale Scene Reconstruction

Tianchen Deng, Chongdi Wang, Nailin Wang, Lei Zhao+5

Neural Radiance Fields (NeRF)-based SLAM has demonstrated impressive results in small-scale scene reconstruction, yet scaling these methods to extensive, complex environments remains challenging due to catastrophic forgetting and accumulated trajectory drift. This paper presents a robust, large-scale neural SLAM system featuring a multi-submap architecture and a dual-tier loop closure mechanism. Specifically, we propose a progressive mapping strategy that dynamically allocates neural submaps to maintain high-fidelity representations without memory explosion. For robust pose estimation, an optical-flow-based tracking module is integrated to handle aggressive motions. To address global consistency, we introduce a local-to-global loop closure framework leveraging the foundation model for high-performance global descriptor extraction, significantly enhancing relocalization accuracy under varying viewpoints. Furthermore, an inter-submap online distillation algorithm is designed during back-end optimization to enforce geometric and appearance consistency across overlapping submap boundaries. To validate the system, we developed a customized handheld mechatronic platform and conducted extensive evaluations on both public benchmarks and our large-scale indoor-outdoor datasets. Experimental results, including direct deployment on an onboard computing unit, demonstrate that our approach outperforms state-of-the-art neural SLAM methods in reconstruction quality and localization robustness, providing a scalable solution for real-world robotic perception and digital twinning. We will release the code publicly on \href{https://github.com/dtc111111/MSN-SLAM}{https://github.com/dtc111111/MSN-SLAM} .

Simultaneous Localization And Mapping Dynamic Scene Reconstruction Neural Radiance Fields Robotic Perception

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Abstract

Explore similar work

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