两种SLAM算法在无特征环境下使用蛇形运动的影响

Yang Tian, Victor Gomez, Shugen Ma
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引用次数: 13

摘要

蛇形机器人能够以不同的运动模式在狭窄的空间中移动,即使是不平坦的地形或隧道或管道等高度受限的环境。在这样的空间定位是困难的,特别是因为缺乏识别运动或区域细节的特征。在这些没有特征的环境中,没有里程计的机器人将类似于一个新的绑架问题。本文介绍了在无特征环境下,同时定位和映射(SLAM)算法在没有绑架恢复算法的情况下,如何在一定的运动(如蛇形运动)后产生影响。通过对两种相似SLAM算法的比较,展示了仅存在于其中一种SLAM算法中的关键过程如何影响映射性能。并对工艺参数进行了修改试验。此外,还进行了改变地图网格大小时的模拟。仿真和实验表明了分析的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influence of two SLAM algorithms using serpentine locomotion in a featureless environment
Snake-like robots are capable of different locomotion patterns to move in narrow spaces, even with uneven terrain or highly constrained environments such as tunnels or pipes. Localization in such spaces is difficult, especially because of the lack of features to recognize movement or the area specifics. In these featureless environments, odometry-less robots will be similar to a new kidnapping problem. In this paper, how the influence of a Simultaneous Localization and Mapping (SLAM) algorithm is had in featureless environments without kidnapping recovery algorithms following a certain locomotion, such as the serpentine locomotion, is presented. In a comparison between two similar SLAM algorithms, how a key process, which is only present in one of SLAM algorithms, can affect the mapping performance, is shown. Furthermore, tests modifying the parameters of this process have been done. Also, simulations while changing the grid size of the map were conducted. Simulations and experiments have been done to show the validity of our analysis.
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