LSH-RANSAC: Incremental Matching of Large-Size Maps
LSH-RANSAC: Incremental Matching of Large-Size Maps
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DOI：
10.1587/transinf.e93.d.326
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发表时间：
2010-02
期刊：
影响因子：
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通讯作者：
Kanji Tanaka;Kenichi Saeki;M. Minami;Takeshi Ueda
中科院分区：
文献类型：
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作者：
Kanji Tanaka;Kenichi Saeki;M. Minami;Takeshi Ueda
本文提出了一种新的方法，机器人定位使用地标地图。随着SLAM研究的最新进展，机器人获取和使用由其他制图机器人增量构建的大尺寸地图已变得至关重要。我们的本地化方法成功地与这样的增量和大尺寸的地图。在文献中，RANSAC地图匹配已经成为一种很有前途的大尺寸地图匹配方法。我们扩展RANSAC地图匹配，以便处理增量地图。我们结合联合收割机的增量RANSAC与增量LSH数据库，并开发一个混合的基于位置和外观的方法。一系列使用萝卜数据集的实验显示了可喜的结果。
This paper presents a novel approach for robot localization using landmark maps. With recent progress in SLAM researches, it has become crucial for a robot to obtain and use large-size maps that are incrementally built by other mapper robots. Our localization approach successfully works with such incremental and large-size maps. In literature, RANSAC map-matching has been a promising approach for large-size maps. We extend the RANSAC map-matching so as to deal with incremental maps. We combine the incremental RANSAC with an incremental LSH database and develop a hybrid of the position-based and the appearance-based approaches. A series of experiments using radish dataset show promising results.