Towards a computational theory of cognitive maps
Artificial Intelligence
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A reader for the cognitive map
Information Sciences: an International Journal
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
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
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Spatial Cognition and Computation
Exploring artificial intelligence in the new millennium
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM
International Journal of Computer Vision
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Coarse-to-fine vision-based localization by indexing scale-Invariant features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
Information Sciences: an International Journal
Image-based homing navigation with landmark arrangement matching
Information Sciences: an International Journal
Tracking a moving object via a sensor network with a partial information broadcasting scheme
Information Sciences: an International Journal
Information Sciences: an International Journal
Holography map for home robot: an object-oriented approach
Intelligent Service Robotics
Fast and scalable approximate spectral graph matching for correspondence problems
Information Sciences: an International Journal
A comparison of EKF and SGD applied to a view-based SLAM approach with omnidirectional images
Robotics and Autonomous Systems
Hi-index | 0.07 |
This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.