Efficient Dense Frontier Detection for 2D Graph SLAM Based on Occupancy Grid Submaps
This work addresses the challenge of efficient frontier detection for autonomous robots, but it appears incremental as it builds on existing SLAM and frontier detection concepts.
The paper tackles the problem of frontier detection in autonomous robot exploration by proposing a specialized method that is efficiently constrained to active submaps and robust to SLAM loop closures, resulting in improved efficiency for 2D graph SLAM based on occupancy grid submaps.
In autonomous robot exploration, the frontier is the border in the world map between the explored space and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We examine a modular control system pipeline for autonomous exploration where a 2D graph SLAM algorithm based on occupancy grid submaps performs map building and localization. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a specialized frontier detection method which is efficiently constrained to active submaps, yet robust to SLAM loop closures.