title: Probabilistic double guarantee kidnapping detection in SLAM.
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.
authors.affiliation: Department of Robotics, Ritsumeikan University, Shiga, 525-8577 Japan.
authors.forename: Yang
authors.identifier: 0000-0003-1354-2143
authors.initials: Y
authors.lastname: Tian
authors.affiliation: Department of Robotics, Ritsumeikan University, Shiga, 525-8577 Japan ; Department of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 China.
authors.forename: Shugen
authors.initials: S
authors.lastname: Ma
keywords: Autonomous mobile robots; Kidnapping detection; Simultaneous localization and mapping