LSH-RANSAC: An incremental scheme for scalable localization
LSH-RANSAC: An incremental scheme for scalable localization
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DOI：
10.1109/robot.2009.5152201
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
Kenichi Saeki;Kanji Tanaka;Takeshi Ueda
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文献类型：
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作者：
Kenichi Saeki;Kanji Tanaka;Takeshi Ueda
本文研究了大规模环境下基于特征的机器人定位问题。随着SLAM技术的最新进展，对于机器人来说，相对于可以由其他映射器机器人递增地构建的大尺寸地图实时估计自身位置已经变得至关重要。在文献中已经研究了使用大尺寸地图的自定位，但是大多数他们假设在自定位任务之前给出完整的地图。在本文中，我们提出了一种新的计划，机器人定位以及地图表示，可以成功地与大尺寸和增量地图。这项工作结合了我们以前的两个工作增量方法，iLSH和iRANSAC，基于外观和基于位置的定位。
This paper addresses the problem of feature-based robot localization in large-size environments. With recent progress in SLAM techniques, it has become crucial for a robot to estimate the self-position in real-time with respect to a large-size map that can be incrementally build by other mapper robots. Self-localization using large-size maps have been studied in litelature, but most of them assume that a complete map is given prior to the self-localization task. In this paper, we present a novel scheme for robot localization as well as map representation that can successfully work with large-size and incremental maps. This work combines our two previous works on incremental methods, iLSH and iRANSAC, for appearance-based and position-based localization.