Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Illumination Planning for Object Recognition Using Parametric Eigenspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Artificial intelligence and mobile robots
Position estimation for mobile robots in dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Digital Image Processing
Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
VisionBug: A Hexapod Robot Controlled by Stereo Cameras
Autonomous Robots
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Omnidirectional Vision for Appearance-Based Robot Localization
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Robust Localization Using Eigenspace of Spinning-Images
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Comparing image-based localization methods
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
International Journal of Robotics Research
Action evaluation for mobile robot global localization in cooperative environments
Robotics and Autonomous Systems
A new pyramid-based color image representation for visual localization
Image and Vision Computing
Localization: approximation and performance bounds to minimize travel distance
IEEE Transactions on Robotics
Augmenting appearance-based localization and navigation using belief update
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 2 - Volume 2
Parallelization of Belief Propagation on Cell Processors for Stereo Vision
ACM Transactions on Embedded Computing Systems (TECS)
Multi-observation sensor resetting localization with ambiguous landmarks
Autonomous Robots
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A vision-based robot localization system must be robust: able to keep track of the position of the robot at any time even if illumination conditions change and, in the extreme case of a failure, able to efficiently recover the correct position of the robot. With this objective in mind, we enhance the existing appearance-based robot localization framework in two directions by exploiting the use of a stereo camera mounted on a pan-and-tilt device. First, we move from the classical passive appearance-based localization framework to an active one where the robot sometimes executes actions with the only purpose of gaining information about its location in the environment. Along this line, we introduce an entropy-based criterion for action selection that can be efficiently evaluated in our probabilistic localization system. The execution of the actions selected using this criterion allows the robot to quickly find out its position in case it gets lost. Secondly, we introduce the use of depth maps obtained with the stereo cameras. The information provided by depth maps is less sensitive to changes of illumination than that provided by plain images. The main drawback of depth maps is that they include missing values: points for which it is not possible to reliably determine depth information. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to depth maps. The experiments we present show that the combination of the active localization with the use of depth maps gives an efficient and robust appearance-based robot localization system.