Incremental algorithms for Safe and Reachable Frontier Detection for robot exploration

Abstract

Majority of the autonomous robot exploration strategies operate by iteratively extracting the boundary between the mapped open space and unexplored space, frontiers, and sending the robot towards the “best” frontier. Traditional approaches process the entire map to retrieve the frontier information at each decision step. This operation however is not scalable to large map sizes and high decision frequencies. In this article, a computationally efficient incremental approach, Safe and Reachable Frontier Detection (SRFD), that processes locally updated map data to generate only the safe and reachable (i.e. valid) frontier information is introduced. This is achieved by solving the two sub-problems of a) incrementally updating a database of boundary contours between mapped-free and unknown cells that are safe for robot and b) incrementally identifying the reachability of the contours in the database. Only the reachable boundary contours are extracted as frontiers. Experimental evaluation on real world data sets validate that the proposed incremental update strategy provides a significant improvement in execution time while maintaining the global accuracy of frontier generation. The low computational footprint of proposed frontier generation approach provides the opportunity for exploration strategies to process frontier information at much higher frequencies which could be used to generate more efficient exploration strategies.