Abstract

Abstract—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 which 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 EKF linearizations. The memory requirements are also linear with the size of the map. As the algorithm works in covariance form, well-known data association techniques can be used in the usual manner. We present experimental results using a hand-held monocular camera, building a map along a closed loop trajectory of 140m in a public square, with people and other clutter. Our results show that the combination of conditional independence, that enables the system to share camera and feature states between submaps, and local coordinates, that reduce the effects of linearization errors, allow us to obtain precise maps of large areas with pure monocular SLAM in real time.

Citations