coVoxSLAM利用GPU加速构建大场景全局一致的稠密地图，提升机器人定位精度与实时性。
coVoxSLAM: GPU Accelerated Globally Consistent Dense SLAM
- 基于GPU并行计算实现体素化稠密建图，提升处理效率。
- 在多个公开数据集上实测，执行时间显著缩短且定位准确。
- 支持桌面与嵌入式GPU平台，适合移动机器人实时导航应用。
稠密SLAM系统对移动机器人至关重要，可提供定位能力，支持在非结构化环境中进行导航、路径规划、避障及决策。随着计算需求增加，GPU在稠密SLAM中的应用日益广泛。本文提出coVoxSLAM，一种新型GPU加速的体素化SLAM系统，充分利用GPU的并行处理能力，在大规模环境中构建全局一致的地图。该系统已在不同平台（独立显卡与嵌入式GPU）部署，并与现有最先进方法对比。使用公开数据集的实验结果表明，coVoxSLAM在显著降低执行时间的同时保持了高定位精度。系统已开源，代码地址：https://github.com/lrse-uba/coVoxSLAM。
原文摘要 · Abstract (English)
A dense SLAM system is essential for mobile robots, as it provides localization and allows navigation, path planning, obstacle avoidance, and decision-making in unstructured environments. Due to increasing computational demands the use of GPUs in dense SLAM is expanding. In this work, we present coVoxSLAM, a novel GPU-accelerated volumetric SLAM system that takes full advantage of the parallel processing power of the GPU to build globally consistent maps even in large-scale environments. It was deployed on different platforms (discrete and embedded GPU) and compared with the state of the art. The results obtained using public datasets show that coVoxSLAM delivers a significant performance improvement considering execution times while maintaining accurate localization. The presented system is available as open-source on GitHub https://github.com/lrse-uba/coVoxSLAM.
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。