A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs

Authors:
Stephen Tully;George Kantor;Howie Choset;Felix Werner
Affiliations:
Electrical and Computer Engineering Department at Carnegie Mellon University, Pittsburgh, PA;Robotics Institute at Carnegie Mellon University, Pittsburgh, PA;Robotics Institute at Carnegie Mellon University, Pittsburgh, PA;Information Technology Department at Queensland University of Technology, Brisbane, Australia
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 4
Cited 2

Occupancy grids: a probabilistic framework for robot perception and navigation

Occupancy grids: a probabilistic framework for robot perception and navigation
Towards a general theory of topological maps

Artificial Intelligence
The hierarchical atlas

IEEE Transactions on Robotics
Bayesian inference in the space of topological maps

IEEE Transactions on Robotics

A unified Bayesian framework for global localization and SLAM in hybrid metric/topological maps

International Journal of Robotics Research
A pure vision-based topological SLAM system

International Journal of Robotics Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a method for topological SLAM that specifically targets loop closing for edge-ordered graphs. Instead of using a heuristic approach to accept or reject loop closing, we propose a probabilistically grounded multihypothesis technique that relies on the incremental construction of a map/state hypothesis tree. Loop closing is introduced automatically within the tree expansion, and likely hypotheses are chosen based on their posterior probability after a sequence of sensor measurements. Careful pruning of the hypothesis tree keeps the growing number of hypotheses under control and a recursive formulation reduces storage and computational costs. Experiments are used to validate the approach.