Abstract
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 this https URL.
Abstract (translated)
一个密集的SLAM系统对于移动机器人至关重要，因为它提供了定位功能，并允许在非结构化环境中进行导航、路径规划、障碍物规避和决策。由于计算需求不断增加，GPU在密集SLAM中的应用正在扩大。在这项工作中，我们介绍了一种新型的GPU加速体积SLAM系统——coVoxSLAM，它充分利用了GPU的并行处理能力，在大规模环境中构建全局一致的地图。该系统部署在不同的平台（离散和嵌入式GPU）上，并与现有技术进行了比较。使用公共数据集获得的结果表明，考虑到执行时间，coVoxSLAM提供了显著的性能改进，同时保持了准确的定位。该系统作为开源项目发布在GitHub上，链接为：[此处提供实际URL]。 注：最后的链接需要替换为实际的GitHub URL地址。
URL
https://arxiv.org/abs/2410.21149
