Hierarchical SLAM: real-time accurate mapping of large environments
Top Cited Papers
- 8 August 2005
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Robotics
- Vol. 21 (4) , 588-596
- https://doi.org/10.1109/tro.2005.844673
Abstract
In this paper, we present a hierarchical mapping method that allows us to obtain accurate metric maps of large environments in real time. The lower (or local) map level is composed of a set of local maps that are guaranteed to be statistically independent. The upper (or global) level is an adjacency graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained at this level in a relative stochastic map. We propose a close to optimal loop closing method that, while maintaining independence at the local level, imposes consistency at the global level at a computational cost that is linear with the size of the loop. Experimental results demonstrate the efficiency and precision of the proposed method by mapping the Ada Byron building at our campus. We also analyze, using simulations, the precision and convergence of our method for larger loops.Keywords
This publication has 20 references indexed in Scilit:
- Linear time vehicle relocation in SLAMPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A system for volumetric robotic mapping of abandoned minesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Results for outdoor-SLAM using sparse extended information filtersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Incremental mapping of large cyclic environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Towards constant time SLAM using postponementPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Building a million beacon mapPublished by SPIE-Intl Soc Optical Eng ,2001
- Optimization of the simultaneous localization and map-building algorithm for real-time implementationIEEE Transactions on Robotics and Automation, 2001
- Mobile Robot Localization and Map BuildingPublished by Springer Nature ,1999
- A Probabilistic Approach to Concurrent Mapping and Localization for Mobile RobotsMachine Learning, 1998
- Globally Consistent Range Scan Alignment for Environment MappingAutonomous Robots, 1997