NDLI: Monte Carlo Localization and registration to prior data for outdoor navigation

Content Provider	IEEE Xplore Digital Library
Author	Silver, D. Stentz, A.
Copyright Year	2011
Description	Author affiliation: Carnegie Mellon University, USA (Silver, D.; Stentz, A.)
Abstract	GPS has become the de facto standard for obtaining a global position estimate during outdoor autonomous navigation. However, GPS can become degraded due to occlusion or interference, to the detriment of autonomous performance. In addition, GPS positions must be aligned with prior data, a tedious and continual process. This work presents a solution to these two problems based on learning generic observation models in the presence of GPS to use in its absence. The models are non-parametric and compared to traditional approaches require few assumptions about either the prior data available or a robot's onboard sensors. Along with allowing for localization to prior data under GPS-denied conditions, this learning approach can be coupled with an EM procedure to automatically register GPS and prior data positions. Experimental results are presented based on data from more than 15 km of autonomous navigation through challenging outdoor terrain.
Starting Page	510
Ending Page	517
File Size	1461959
Page Count	8
File Format	PDF
ISBN	9781612844541
ISSN	21530858
e-ISBN	9781612844565
DOI	10.1109/IROS.2011.6094532
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Global Positioning System Robot sensing systems Uncertainty Data models
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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