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
1-point RANSAC for EKF-based structure from motion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Smoothing-based submap merging in large area SLAM
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Real-time optical SLAM-based mosaicking for unmanned underwater vehicles
Intelligent Service Robotics
Editors Choice Article: Visual SLAM: Why filter?
Image and Vision Computing
Local map-based exploration for mobile robots
Intelligent Service Robotics
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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.