Machine Learning - Special issue on learning with probabilistic representations
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Model-Based Object Recognition - A Survey of Recent Research
Model-Based Object Recognition - A Survey of Recent Research
Learning with mixtures of trees
The Journal of Machine Learning Research
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Randomized RANSAC with Sequential Probability Ratio Test
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
3D City Modeling Using Cognitive Loops
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Online generation of scene descriptions in urban environments
Robotics and Autonomous Systems
Relational object maps for mobile robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Original paper: Stereo vision with texture learning for fault-tolerant automatic baling
Computers and Electronics in Agriculture
Classification and Semantic Mapping of Urban Environments
International Journal of Robotics Research
Semantic parsing of street scenes from video
International Journal of Robotics Research
PLISS: labeling places using online changepoint detection
Autonomous Robots
Topological map induction using neighbourhood information of places
Autonomous Robots
Active exploration for robust object detection
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Online semantic mapping of urban environments
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
Indoor scene recognition by a mobile robot through adaptive object detection
Robotics and Autonomous Systems
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This paper introduces a multi-level classification framework for the semantic annotation of urban maps as provided by a mobile robot. Environmental cues are considered for classification at different scales. The first stage considers local scene properties using a probabilistic bag-of-words classifier. The second stage incorporates contextual information across a given scene (spatial context) and across several consecutive scenes (temporal context) via a Markov Random Field (MRF). Our approach is driven by data from an onboard camera and 3D laser scanner and uses a combination of visual and geometric features. By framing the classification exercise probabilistically we take advantage of an information-theoretic bail-out policy when evaluating class-conditional likelihoods. This efficiency, combined with low order MRFs resulting from our two-stage approach, allows us to generate scene labels at speeds suitable for online deployment. We demonstrate the virtue of considering such spatial and temporal context during the classification task and analyze the performance of our technique on data gathered over almost 17 km of track through a city.