SLAM WITH CROWD-SOURCED DATA

摘要

Simultaneous localization and mapping (SLAM) is a cornerstone for a plethora of applications in robotics and augmented reality. It can be done with visual data collected by a single camera. Alternatively, if the visual data are crowd-sourced by multiple cameras, collaborative SLAM presents a more appealing solution. Its benefits are: (1) Each camera user can navigate based on the map built by other users; (2) The computation is shared by many processing units. This paper presents a collaborative SLAM approach with the following attributes: (1) Multiple users can localize within and work with a map merged from maps built by each user due to commonly visited areas; (2) The size of the map grows only if a new area is explored; (3) A robust loop closure approach solves for subsets of variables involved in the pose graph step by step.