NeoSLAM的一种新实现及与RatSLAM的比较评估
A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM
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中文总结 AI 辅助
研究提出NeoSLAM算法新实现，将其重写为模块化架构以实时执行。通过在三个数据集上对比NeoSLAM与RatSLAM，凸显二者在映射与轨迹重建差异，新NeoSLAM在实时处理吞吐量上超原版本，地图重建性能与RatSLAM相当。
中文摘要 AI 辅助
本文提出了NeoSLAM算法的一种新实现。该版本将NeoSLAM完全重写成模块化架构，使用现代框架，能在最小化丢弃输入数据的情况下实时执行。还在不同环境条件下的三个数据集上对NeoSLAM和RatSLAM进行比较评估。实验结果凸显了映射一致性和轨迹重建方面的差异，证明了基于ROS2实现的有效性和实际适用性。新NeoSLAM在实时应用处理吞吐量上优于原版本，在地图重建方面与RatSLAM性能相当。
英文摘要
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.
发表机构
- Federal University of Rio de Janeiro（里约热内卢联邦大学）
- Providence College（普罗维登斯学院）
- Florida International University（佛罗里达国际大学）
机构由 AI 辅助整理，请以论文原文为准。
