Multi-robot SLAM with Exactly Sparse Extended Information Filtering

In the robot simultaneous localization and mapping(SLAM) problem, the Multi-robot SLAM has drawn much attention recently in robotics. This paper investigates the Multi-robot SLAM problem in using exactly sparse extended information filters algorithm(ESEIF): Based on both the multi-robot motion model and observation model, the multi-robot pose is estimated and the environment features are observed. The threshold is set for partitioning and updating the observed features, and marginalizing the robot pose followed by relocating the robot. The simulation shows that the robots pose can be accurately estimated, and observation can be updated in a constant time fashion irrespective of the number of features in the map.