An autonomous exploration algorithm using environment-robot interacted traversability analysis

Auto-exploration is a task for self-driving robots to explore unknown environments, which becomes much complicated when they move on irregular outdoor terrains. To improve the situation, a new frontier-based exploration algorithm is presented in this paper. It starts from original 3D cloud points of the environment to analyze the traversability of the scanned area, and further provides a reachability map to mark all map grid cells as reachable, dangerous or unknown. Frontier candidates are obtained from the reachable map, then clustered and reduced using an improved K-means. Finally, the target of next exploration step is selected from the frontiers left by evaluating their travel cost. The algorithm is validated on an irregular outdoor terrain and shows the capability for a field robot to explore on an irregular terrain.