Detecting Loop Closure with Scene Sequences
International Journal of Computer Vision
A Dual Graph Pyramid Approach to Grid-Based and Topological Maps Integration for Mobile Robotics
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Topological SLAM Using Fast Vision Techniques
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Autonomous generation of behavioral trace maps using rescue robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy
International Journal of Robotics Research
Online topological map building and qualitative localization in large-scale environment
Robotics and Autonomous Systems
Image similarity based on Discrete Wavelet Transform for robots with low-computational resources
Robotics and Autonomous Systems
Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment
International Journal of Robotics Research
Optimal Filtering for Non-parametric Observation Models: Applications to Localization and SLAM
International Journal of Robotics Research
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
Online probabilistic topological mapping
International Journal of Robotics Research
A unified Bayesian framework for global localization and SLAM in hybrid metric/topological maps
International Journal of Robotics Research
Topological map induction using neighbourhood information of places
Autonomous Robots
A review of path planning and mapping technologies for autonomous mobile robot systems
Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
Hi-index | 0.00 |
While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding general-purpose methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies, given available sensor measurements. We show that the space of topologies is equivalent to the intractably large space of set partitions on the set of available measurements. The combinatorial nature of the problem is overcome by computing an approximate, sample-based representation of the posterior. The PTM is obtained by performing Bayesian inference over the space of all possible topologies, and provides a systematic solution to the problem of perceptual aliasing in the domain of topological mapping. In this paper, we describe a general framework for modeling measurements, and the use of a Markov-chain Monte Carlo algorithm that uses specific instances of these models for odometry and appearance measurements to estimate the posterior distribution. We present experimental results that validate our technique and generate good maps when using odometry and appearance, derived from panoramic images, as sensor measurements.