SLAM with sparse sensing

Most work on the simultaneous localization and mapping (SLAM) problem assumes the frequent availability of dense information about the environment such as that provided by a laser rangefinder. However, for implementing SLAM in consumer-oriented products such as toys or cleaning robots, it is infeasible to use expensive sensing. In this work we examine the SLAM problem for robots with very sparse sensing that provides too little data to extract features of the environment from a single scan. We modify SLAM to group several scans taken as the robot moves into multiscans, achieving higher data density in exchange for greater measurement uncertainty due to odometry error. We formulate a full system model for this approach, and then introduce simplifications that enable efficient implementation using a Rao-Blackwellized particle filter. Finally, we describe simple algorithms for feature extraction and data association of line and line segment features from multiscans, and then present experimental results using real data from several environments