Autonomous Exploration and Semantic Updating of Large-Scale Indoor Environments with Mobile Robots

Abstract: We introduce a new robotic system that enables a mobile robot to autonomously explore an unknown environment, build a semantic map of the environment, and subsequently update the semantic map to reflect environment changes, such as location changes of objects. Our system leverages a LiDAR scanner for 2D occupancy grid mapping and an RGB-D camera for object perception. We introduce a semantic map representation that combines a 2D occupancy grid map for geometry with a topological map for object semantics. This map representation enables us to effectively update the semantics by deleting or adding nodes to the topological map. Our system has been tested on a Fetch robot, semantically mapping a 93m x 90m and a 9m x 13m indoor environment and updating their semantic maps once objects are moved in the environments