Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs

Abstract

Autonomous exploration and mapping in unknown environments remain pivotal in robotics research. The efficiency of autonomous exploration is often constrained by irrational exploration strategies and incomplete map exploration. This paper proposes an efficient autonomous exploration method based on a frontier strategy, aiming to enhance the performance of ground mobile robots in exploration and mapping tasks. We employ a real-time grid map optimization technique using bilateral filtering and expansion to eliminate inefficient frontiers, improve mapping quality, and enhance the overall efficiency of autonomous exploration. Additionally, we construct a novel frontier cost function that incorporates factors such as path length, sensor measurement range, and information gain. Our approach uniquely combines an autonomous exploration decision model with the Minimum Ratio Travelling Salesman Problem (MRTSP) to maximize the explored area within the shortest possible path. Comparative analyses with classic methods, conducted in both simulated and real environments, demonstrate a 10-30% improvement in exploration efficiency through our approach.