Paper Info
Title
An efficient area-based observation model for Monte-Carlo robot localization
Abstract
The problem of mobile robot self-localization is considered as solved since Thrun's et. al pioneering work using monte-carlo filters for robot Localization (MCL). However, MCL is robust and precise under constraints like completely known environments and the sensor data must contain enough ¿true data¿ as contained in the map. In fact these conditions cannot always be guaranteed, which may results in a poor accuracy of the localization. In this paper we present a area-based observation model that is applied to MCL self-localization. The model is based on the idea of tracking the ground area inside the ¿free space¿ (not occupied cells) of a known map. Experimental data shows that the proposed model improves the robustness and accuracy of laser and stereo vision sensors under certain conditions like incomplete map, limited FOV and limited range of sensing. We also present an efficient approximation of our sensor model based on integral images.
Year
DOI
Venue
2009
10.1109/IROS.2009.5354355
St. Louis, MO
Keywords
Field
DocType
monte-carlo robot localization,true data,sensor model,area-based observation model,efficient area-based observation model,known map,incomplete map,limited fov,sensor data,experimental data,mcl self-localization,image sensors,robustness,accuracy,stereo vision,free space,mobile robot,data mining,tracking,monte carlo,visual perception,computational modeling,monte carlo methods,laser,lasers,robust control
Robot localization,Computer vision,Monte Carlo method,Image sensor,Computer science,Stereopsis,Robustness (computer science),Control engineering,Free space,Artificial intelligence,Robust control,Mobile robot
Conference
ISBN
Citations 
PageRank 
978-1-4244-3804-4
8
0.60
References 
Authors
16
2
Name
Order
Citations
PageRank
Sven Olufs1214.67
Markus Vincze21343136.87