Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM

-- For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect. I. INTRODUCTION Autonomous robots operating in real life settings must be able to navigate in large, unstructured, dynamic and unknown spaces. To do so, they must build a map of their operating environment in order to localize itself in it, a problem known as Simultaneous localization and mapping (SLAM). A key feature in SLAM is detecting previously visited areas to reduce map errors, a process known as loop closure detection. Our interest lies with graph-based SLAM approaches [1] that use nodes as poses and links as odometry and loop closure transformations.