Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
A mobile robot that learns its place
Neural Computation
The interactive museum tour-guide robot
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Supervised dimension reduction of intrinsically low-dimensional data
Neural Computation
Markov localization using correlation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Active Appearance-Based Robot Localization Using Stereo Vision
Autonomous Robots
Robot Homing by Exploiting Panoramic Vision
Autonomous Robots
Lattice independent component analysis for mobile robot localization
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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Mobile robots need an internal representation of their environment to do useful things. Usually such a representation is some sort of geometric model. For our robot, which is equipped with a panoramic vision system, we choose an appearance model in which the sensoric data (in our case the panoramic images) have to be modeled as a function of the robot position. Because images are very high-dimensional vectors, a feature extraction is needed before the modeling step. Very often a linear dimension reduction is used where the projection matrix is obtained from a Principal Component Analysis (PCA). PCA is optimal for the reconstruction of the data, but not necessarily the best linear projection for the localization task. We derived a method which extracts linear features optimal with respect to a risk measure reflecting the localization performance. We tested the method on a real navigation problem and compared it with an approach where PCA features were used.