Paper Info
Title
Mapping and Localization for Mobile Robots through Environment Appearance Update
Abstract
The strength of appearance-based mapping models lies in their ability to represent the environment through high-level image features; and provide humanreadable information. However, developing localization and mapping methods with these models could be very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. This paper proposes an appearance-based mapping and localization method based on the human memory model, which is used to build a Feature Stability Histogram (FSH) at each node in the robot topological map, these FSH register local feature stability over time through a voting scheme, and most stable features are considered for mapping and Bayesian localization. Experimental results are presented using omnidirectional images acquired through long-term acquisition considering: illumination changes (day time and seasons), occlusions, random removal of features, and perceptual aliasing. This method is able to adapt the internal node's representation through time to achieve global and local robot localization.
Year
DOI
Venue
2010
10.3233/978-1-60750-643-0-291
CCIA
Keywords
Field
DocType
mobile robots,local feature stability,mapping method,local robot localization,long-term mapping,environment appearance update,internal node,appearance-based mapping model,appearance-based mapping,bayesian localization,localization method,day time,mobile robot
Histogram,Omnidirectional antenna,Computer vision,Computer science,Feature (computer vision),Aliasing,Artificial intelligence,Topological map,Robot,Mobile robot,Bayesian probability
Conference
Volume
ISSN
Citations 
220
0922-6389
0
PageRank 
References 
Authors
0.34
13
3
Name
Order
Citations
PageRank
B. Bacca1171.66
Joaquim Salvi2144393.90
Xavier Cufí326615.85