A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
ACM Computing Surveys (CSUR)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition Using Composed Receptive Field Histograms of Higher Dimensionality
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Robot Cognition and Navigation: An Experiment with Mobile Robots (Cognitive Technologies)
Robot Cognition and Navigation: An Experiment with Mobile Robots (Cognitive Technologies)
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
Quantitative Evaluation of Feature Extractors for Visual SLAM
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Robotics: Modelling, Planning and Control
Robotics: Modelling, Planning and Control
Semantic place classification of indoor environments with mobile robots using boosting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Multi-modal Semantic Place Classification
International Journal of Robotics Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Contemporary mobile robots should exhibit enhanced capacities, which allow them self-localization and semantic interpretation as they move into an unexplored environment. The coexistence of accurate SLAM and place recognition can provide a descriptive and adaptable navigation model. In this paper such a two-layer navigation scheme is introduced suitable for indoor environments. The low layer comprises a 3D SLAM system based solely on an RGB-D sensor, whilst the high one employs a novel content-based representation algorithm, suitable for spatial abstraction. In course of robot's locomotion, salient visual features are detected and they shape a bag-of-features problem, quantized by a Neural Gas to code the spatial information for each scene. The learning procedure is performed by an SVM classifier able to accurately recognize multiple dissimilar places. The two layers mutually interact with a semantically annotated topological graph augmenting the cognition attributes of the integrated system. The proposed framework is assessed on several datasets, exhibiting remarkable accuracy. Moreover, the appearance based algorithm produces semantic inferences suitable for labeling unexplored environments.