Frontier Detection and Reachability Analysis for Efficient 2D Graph-SLAM Based Active Exploration
This work addresses efficient exploration for mobile robots in indoor settings, but it appears incremental as it builds on existing SLAM methods.
The paper tackles the problem of active exploration for mobile robots by integrating frontier detection and reachability analysis with graph-based SLAM, demonstrating effectiveness and efficiency in real indoor environments.
We propose an integrated approach to active exploration by exploiting the Cartographer method as the base SLAM module for submap creation and performing efficient frontier detection in the geometrically co-aligned submaps induced by graph optimization. We also carry out analysis on the reachability of frontiers and their clusters to ensure that the detected frontier can be reached by robot. Our method is tested on a mobile robot in real indoor scene to demonstrate the effectiveness and efficiency of our approach.