DocumentCode :
1871544
Title :
A scalable algorithm for monte carlo localization using an incremental E2LSH-database of high dimensional features
Author :
Tanaka, Kanji ; Kondo, Eiji
Author_Institution :
Grad. Sch. of Eng., Kyushu Univ., Fukuoka
fYear :
2008
fDate :
19-23 May 2008
Firstpage :
2784
Lastpage :
2791
Abstract :
In recent years, high-dimensional descriptive features have been widely used for feature-based robot localization. However, the space/time costs of building/retrieving the map database tend to be significant due to the high dimensionality. In addition, most of existing databases are working well only on batch problems, difficult to be built incrementally by a mapper robot. In this paper, a scalable localization algorithm is proposed for incremental databases of high dimensional features. The Monte Carlo localization (MCL) algorithm is extended by employing the exact Euclidean locality sensitive hashing (LSH). The robustness and efficiency of the proposed algorithms have been demonstrated using the radish dataset.
Keywords :
Monte Carlo methods; mobile robots; path planning; robust control; Euclidean locality sensitive hashing; Monte Carlo localization; high-dimensional descriptive features; incremental E2LSH-database; radish dataset; robot localization; robustness; scalable localization algorithm; Costs; Image databases; Information retrieval; Monte Carlo methods; Robot localization; Robot sensing systems; Robotics and automation; Simultaneous localization and mapping; Spatial databases; USA Councils;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on
Conference_Location :
Pasadena, CA
ISSN :
1050-4729
Print_ISBN :
978-1-4244-1646-2
Electronic_ISBN :
1050-4729
Type :
conf
DOI :
10.1109/ROBOT.2008.4543632
Filename :
4543632
Link To Document :
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