平行SLAM:通过并发处理增强映射和本地化
Francisco J Romero-Ramirez1, Miguel Cazorla2, Manuel J Marín-Jiménez3,4
1Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Campus de Fuenlabrada, Universidad Rey Juan Carlos, 28942 Fuenlabrada, Spain.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本研究介绍了并行映射 (pSLAM) 以加快同时定位和映射 (SLAM) 地图的创建. pSLAM显著加快地图生成速度,以便在不太强大的设备上实时导航.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 同时定位和映射 (SLAM) 系统需要大量的计算资源,限制了低端硬件上的实时应用程序.
- 目前用于SLAM的离线地图创建是连续的,耗时的,即使在强大的计算机上也是如此.
研究的目的:
- 开发一种并行映射方法 (pSLAM),以显著减少线下SLAM地图生成所需的时间.
- 为了能够更快地部署SLAM,在资源有限的设备上实现实时导航.
主要方法:
- 拟议的pslam方法将视频序列划分为多个子序列,用于独立处理.
- 从子序列生成的单个子地图被合并以创建一个统一的全球地图.
- 在各种场景中进行实验验证,并与最先进的SLAM方法进行比较 (UcoSLAM,OpenVSLAM,ORB-SLAM3).
主要成果:
- pSLAM实现了比顺序方法快6倍的处理速度.
- 该方法的稳定性与传统的顺序SLAM相提并论.
- 在所有评估的场景中,pSLAM的表现都超过了UcoSLAM,OpenVSLAM和ORB-SLAM3.
结论:
- 平行处理显著加速了SLAM的离线地图创建.
- pSLAM提供了一种可行的解决方案,用于在低端系统上使用预生成的地图进行高效的实时导航.
- 与现有的最先进的SLAM技术相比,提出的方法显示出更高的性能和速度.
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