新版本NeoSLAM在ROS2上实现，实时性更强且地图重建媲美RatSLAM。
A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM
- 基于ROS2重构为模块化架构，减少数据丢弃，提升实时性。
- 在三个数据集上实测，处理吞吐量优于原版NeoSLAM。
- 地图重建效果接近RatSLAM，适合需要高效建图的机器人应用。
本文提出一个全新的NeoSLAM算法实现。该版本采用现代框架对NeoSLAM进行彻底重写，构建为模块化架构，结合多个技术组件，实现了低数据丢弃下的实时运行。本研究还对NeoSLAM与RatSLAM在三个数据集、不同环境条件下进行了对比评估。实验结果揭示了两者在地图一致性与轨迹重建方面的差异，验证了所提出的基于ROS2实现的有效性与实用性。结果显示，新版本NeoSLAM在实时应用中的处理吞吐量优于原始版本，且在地图重建性能上可与RatSLAM相媲美。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。