Fast and Safe Exploration via Adaptive Semantic Perception in Outdoor Environments

Abstract

Autonomous exploration in unknown environments is a fundamental task for robots. Existing approaches mostly were concentrated on the efficiency of the exploration with the assumption of perfect state estimation, but the drift of pose estimation in visual SLAM occurs frequently and is detrimental to robot's localization and exploration performance. In this paper, a perception-aware exploration(PAE) method is proposed for rapidly and safely autonomous exploration in outdoor environments. The adaptive semantic information is proposed to improve the robustness of perception. Based on the perception module, both the selection of exploration goal on a novel weighted information gain and path planning can avoid the areas with high localization uncertainty. In addition, thanks to the proposed pipeline, including scan-based frontier detection, kd-tree based map prediction and suboptimal frontier buffer strategy, the PAE planner can explore the environment with high success rate and high efficiency. Several simulations are performed to verify the effectiveness of our methods.