调整ROS映射参数及其对室内二维SLAM的影响的定量研究

Y. Abdelrasoul, A. B. Saman, P. Sebastian
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引用次数: 54

摘要

同时定位与制图(SLAM)复杂性降低是一个快速发展的研究领域。其吸引力在于开发低成本但高效的基于SLAM的机器人应用的潜在商业效益。ROS映射包提供了FastSLAM 2.0的轻量级组合。该软件包已用于不同的ROS支持的机器人平台,并显示出显着的成功。然而,封装映射参数的影响似乎没有得到充分利用,特别是在没有完全ROS支持的低成本机器人平台上,如Hercules平台。本文介绍了在标准PC机和树莓派处理器上运行的gmap包的完整实现和性能定量评估。我们通过将这些参数分别改变为几个增量值并在记录的数据集上运行算法来研究调整粒子数量,位移更新和重采样阈值的效果。对于每次运行,构建一个网格映射,并根据映射精度、CPU负载和内存消耗评估性能。然后,我们能够提出一个调优指导方针,以在保持高性能的同时启发映射的执行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A quantitative study of tuning ROS gmapping parameters and their effect on performing indoor 2D SLAM
Simultaneous localization and mapping (SLAM) complexity reduction is a fast progressing research area. Its attraction is owed to the potential commercial benefits of developing low cost yet highly effective SLAM based robotic applications. ROS gmapping package offers a lightweight incorporation of FastSLAM 2.0. The package has been used with different ROS supported robotic platforms and showed remarkable success. However, the effect of the package mapping parameters seem not to be fully exploited, especially with low cost robotic platform with no full ROS support such as Hercules platform. This paper presents a full implementation and performance quantitative evaluation on the gmapping package running on both standard PC and Raspberry Pi processors. We study the effects of tuning the number of particles, the displacement update and the resampling threshold by separately varying each of these parameters to several incremental values and running the algorithm on a recorded dataset. For each run, a grid map was constructed and the performance was evaluated based on mapping accuracy, CPU load and memory consumption. We are then able to propose a tuning guidelines to enlighten the gmapping execution while maintaining high performance.
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