Fast and Safe Exploration via Adaptive Semantic Perception in Outdoor Environments
Fast and Safe Exploration via Adaptive Semantic Perception in Outdoor Environments
复制标题
在户外环境中通过自适应语义感知进行快速、安全的探索
DOI：
10.1109/iros47612.2022.9981640
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xin Jiang
中科院分区：
文献类型：
--
作者：
Zhihao Wang;Lingxu Chen;Hongjin Chen;Haoyao Chen;Xin Jiang
在未知环境中自主探索是机器人的一项基本任务。现有的方法大多集中在完美状态估计假设下的探索效率，但视觉SLAM中位姿估计的漂移经常发生，不利于机器人的定位和探索性能。本文提出了一种感知感知探索（PAE）方法，用于在室外环境中快速、安全地进行自主探索。提出自适应语义信息来提高感知的鲁棒性。基于感知模块，基于新颖的加权信息增益的探索目标选择和路径规划都可以避开具有高定位不确定性的区域。此外，由于所提出的管道，包括基于扫描的边界检测、基于kd树的地图预测和次优边界缓冲策略，PAE规划器可以高成功率和高效率地探索环境。进行了多次模拟来验证我们方法的有效性。
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.
影响因子：
7.8
作者：
Mur-Artal, Raul;Tardos, Juan D.
通讯作者：
Tardos, Juan D.