A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Artificial Intelligence
International Journal of Computer Vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Digital Image Processing
Combining greyvalue invariants with local constraints for object recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Clarification dialogues in human-augmented mapping
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Conceptual spatial representations for indoor mobile robots
Robotics and Autonomous Systems
Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts
Information Sciences: an International Journal
Foundations and Trends in Robotics
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World
International Journal of Robotics Research
A probabilistic framework for object search with 6-DOF pose estimation
International Journal of Robotics Research
Mixing hierarchical contexts for object recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Robotic object detection: learning to improve the classifiers using sparse graphs for path planning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Topological spatial relations for active visual search
Robotics and Autonomous Systems
Holography map for home robot: an object-oriented approach
Intelligent Service Robotics
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The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.