The Common State Filter for SLAM

— This paper presents the Common State Filter (CSF), a novel and efficient suboptimal Multiple Hypothesis SLAM (MHSLAM) method for Kalman Filter-based SLAM algorithms. Conventional MHSLAM algorithms require the entire vehicle and map state to be copied for each hypothesis. The CSF, by contrast, maintains a single, common instance of the vast majority of the map and only copies the map portion that varies substantially across different hypotheses. We demonstrate the performance of the algorithm on the Victoria Park data set.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Martin P. Parsley, Simon J. Julier
• Keywords: Common State Filter, Filter-based SLAM Algorithms, IROS 2008, Multiple Hypothesis SLAM, Robotics