<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The point of interest in this paper is the main content of autonomous navigation of robots. An algorithm for robot Simultaneous Localization And Mapping (SLAM) based on self-detected waypoint is introduced to realize robot’s mapping in its area of interest. Robot’s next step waypoint is performed using characteristics of large information in the area of interest and dense landmark, clustering the landmark in the area of interest, and guiding robot’s movement with clustered central point. Robot clusters the observed area in its observation every time. It takes the clustered center based on the largest number of landmarks as the waypoint of the next step. Simulation experiment shows, that due to robot’s movement toward the area of dense landmarks, the proposed method increases the number of landmarks observed by the robot and frequency of observation is increased. The proposed method enhances accuracy of robot’s positioning and the robot realizes to detect its waypoint autonomously.</p></abstract>

The point of interest in this paper is the main content of autonomous navigation of robots. An algorithm for robot Simultaneous Localization And Mapping (SLAM) based on self-detected waypoint is introduced to realize robot’s mapping in its area of interest. Robot’s next step waypoint is performed using characteristics of large information in the area of interest and dense landmark, clustering the landmark in the area of interest, and guiding robot’s movement with clustered central point. Robot clusters the observed area in its observation every time. It takes the clustered center based on the largest number of landmarks as the waypoint of the next step. Simulation experiment shows, that due to robot’s movement toward the area of dense landmarks, the proposed method increases the number of landmarks observed by the robot and frequency of observation is increased. The proposed method enhances accuracy of robot’s positioning and the robot realizes to detect its waypoint autonomously.

Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint

Faculty of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint"}

The point of interest in this paper is the main content of autonomous navigation of robots. An algorithm for robot Simultaneous Localization And Mapping (SLAM)...

Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint

Publié en ligne: 22 juin 2016

Pages: 212 - 221

DOI: https://doi.org/10.1515/cait-2016-0031

Mots clés
Robot, Simultaneous Localization And Mapping (SLAM), K-means clustering, Extended Kalman Filter (EKF), waypoint

© by Yingmin Yi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Robot Simultaneous Localization and Mapping Based on Self-Detected Waypoint

Publié en ligne: 22 juin 2016

Pages: 212 - 221

DOI: https://doi.org/10.1515/cait-2016-0031

Mots clésRobot, Simultaneous Localization And Mapping (SLAM), K-means clustering, Extended Kalman Filter (EKF), waypoint

© by Yingmin Yi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Robot, Simultaneous Localization And Mapping (SLAM), K-means clustering, Extended Kalman Filter (EKF), waypoint