Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
CI-graph: an efficient approach for large scale SLAM
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
CI-graph: an efficient approach for large scale SLAM
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Sparse Local Submap Joining Filter for Building Large-Scale Maps
IEEE Transactions on Robotics
Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision
IEEE Transactions on Robotics
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
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This paper concerns simultaneous localization and mapping (SLAM) of large areas. In SLAM the map creation is based on identified landmarks in the environment. When mapping large areas a vast number of landmarks have to be treated, which usually is very time consuming. A common way to reduce the computational complexity is to divide the visited area into submaps, each with a limited number of landmarks. This paper presents a novel method for merging conditionally independent submaps (generated using e.g. EKF-SLAM) by the use of smoothing. By this approach it is possible to build large maps in close to linear time. The approach is demonstrated in two indoor scenarios, where data was collected with a trolley-mounted stereo vision camera.