On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
On the Accuracy of Zernike Moments for Image Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
Robotics and Autonomous Systems
Detecting Loop Closure with Scene Sequences
International Journal of Computer Vision
Appearance-based localization for mobile robots using digital zoom and visual compass
Robotics and Autonomous Systems
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Artificial Intelligence Research
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Use of an Autonomous Mobile Robot for Elderly Care
AT-EQUAL '10 Proceedings of the 2010 Advanced Technologies for Enhancing Quality of Life
From Sensors to Human Spatial Concepts: An Annotated Data Set
IEEE Transactions on Robotics
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
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A framework employing the Student-t pdf is introduced for offline map estimation and robot localization using visual loop closures. The framework uses the Student-t pdf (a) as an observation model of a Hidden Markov Model to represent a topological map (b) to represent the robot motion model. The map and the motion model are calculated in an expectation maximization (EM) framework. We show that the estimator converges at linear time and that the provided accuracy is higher compared to using a conventional Gaussian mixture pdf, due to higher noise resiliency, as well as compared to using a fixed robot motion model. The task is assisted by unsupervised landmark definition through the EM-based clustering of the observations and by scene representation using the complex Zernike moments, which provide rich rotation-invariant information. The validity of the method has been verified experimentally using the input from an omnidirectional camera.