Title
Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision
Abstract
Simultaneous localization and mapping (SLAM) algorithms based on local maps have been demonstrated to be well suited for mapping large environments as they reduce the computational cost and improve the consistency of the final estimation. The main contribution of this paper is a novel submapping technique that does not require independence between maps. The technique is based on the intrinsic structure of the SLAM problem that allows the building of submaps that can share information, remaining conditionally independent. The resulting algorithm obtains local maps in constant time during the exploration of new terrain and recovers the global map in linear time after simple loop closures without introducing any approximations besides the inherent extended Kalman filter linearizations. The memory requirements are also linear with the size of the map. As the algorithm works in a covariance form, well-known data-association techniques can be used in the usual manner. We present experimental results using a handheld monocular camera, building a map along a closed-loop trajectory of 140 m in a public square, with people and other clutter. Our results show that the combination of conditional independence, which enables the system to share the camera and feature states between submaps, and local coordinates, which reduce the effects of linearization errors, allow us to obtain precise maps of large areas with pure monocular SLAM in real time.
Year
DOI
Venue
2008
10.1109/TRO.2008.2004636
IEEE Transactions on Robotics
Keywords
Field
DocType
Large-scale systems,Simultaneous localization and mapping,Cameras,Trajectory,Linear approximation,Covariance matrix,Computational efficiency,Buildings,Real time systems,Scalability
Monocular vision,Computer vision,Local coordinates,Global Map,Conditional independence,Kalman filter,Artificial intelligence,Simultaneous localization and mapping,Time complexity,Linearization,Mathematics
Journal
Volume
Issue
ISSN
24
5
1552-3098
Citations 
PageRank 
References 
42
2.08
32
Authors
2
Name
Order
Citations
PageRank
Pedro Pinies118114.45
Juan Domingo23319258.54