同步定位与地图构建中的概率性双重保障绑架检测

Probabilistic double guarantee kidnapping detection in SLAM.

作者信息

Tian Yang, Ma Shugen

机构信息

Department of Robotics, Ritsumeikan University, Shiga, 525-8577 Japan.

Department of Robotics, Ritsumeikan University, Shiga, 525-8577 Japan ; Department of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 China.

出版信息

Robotics Biomim. 2016;3(1):20. doi: 10.1186/s40638-016-0053-z. Epub 2016 Nov 24.

DOI:10.1186/s40638-016-0053-z

PMID:27942433

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5122623/

Abstract

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

摘要

为了在机器人于未知环境中执行自主任务时确定是否发生了绑架以及绑架的类型，提出了一种双保障绑架检测（DGKD）方法。展示了DGKD在相对较小环境中的良好性能。然而，我们最近的工作发现DGKD在大规模环境中存在局限性。为了提高DGKD在大规模环境中的适应性，本文提出了一种改进方法，即概率双保障绑架检测，以结合特征位置的概率和机器人的姿态。仿真结果证明了该方法的有效性和准确性。