scholarly journals An improved graph-based visual localization system for indoor mobile robot using newly designed markers

2018 ◽  
Vol 15 (2) ◽  
pp. 172988141876919 ◽  
Author(s):  
Yuehua Li ◽  
Shiqiang Zhu ◽  
Yiqi Yu ◽  
Zhi Wang
Keyword(s):  
Mobile Robot ◽  
Visual Localization ◽  
Localization System ◽  
Indoor Mobile Robot

scholarly journals Localization System for Indoor Mobile Robot Using Large Square-Shaped Reflective Marker

2021 ◽  
Vol 15 (2) ◽  
pp. 182-190
Author(s):  
Hiroaki Seki ◽  
Ken Kawai ◽  
Masatoshi Hikizu ◽  
◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robot Navigation ◽  
Prototype System ◽  
Localization System ◽  
Indoor Mobile Robot ◽  
Four Corners ◽  
Fisheye Camera ◽  
Position And Orientation ◽  
Marker Detection ◽  
Measurable Range

A localization system using reflective markers and a fisheye camera with blinking infrared lights is useful and safe for mobile robot navigation in an environment with coexisting humans and robots; however, it has the problems of low robustness and a small measurable range for marker detection. A large, square-shaped reflective marker, with solid and dotted edges, is proposed for more reliable localization of indoor mobile robots. It can be easily detected using Hough transform and is robust for occlusion. The coordinates of the four corners of the square-shaped marker determine the robot’s localization. Infrared lighting with a new LED arrangement is designed for a wide measurable range via brightness simulation, including the effect of observation and reflection angles. A prototype system was developed, enabling the 2D position and orientation to be detected with an accuracy of 60 mm and 3◦, respectively, within a 4 m2 area.

Auto-localization system for indoor mobile robot using RFID fusion

Robotica ◽  
2014 ◽  
Vol 33 (9) ◽  
pp. 1899-1908 ◽  
Author(s):  
A. Abdelgawad
Keyword(s):  
Mobile Robot ◽  
Radio Frequency Identification ◽  
Accurate Estimation ◽  
Localization System ◽  
Accurate Localization ◽  
Indoor Mobile Robot ◽  
Current Location ◽  
Frequency Identification ◽  
Passive Rfid ◽  
Estimated Error

SUMMARYAutonomous mobile robots need accurate localization techniques to perform assigned task. Radio Frequency Identification Technology (RFID) has become one of the main means to construct a real-time localization system. Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. This paper proposes an auto-localization system for indoor mobile robot using passive RFID. The proposed system reads any three different RFID tags which have a known location. The current location can be estimated using the Time Difference of Arrival (TDOA) scheme. In order to improve the system accuracy, the proposed system fuses the TDOA scheme for the three tags. A Kalman filter is used to minimize the estimated error and predict the next location. The simulation results validate the proposed framework.

A high precision visual localization sensor and its working methodology for an indoor mobile robot

2016 ◽  
Vol 17 (4) ◽  
pp. 365-374 ◽  
Author(s):  
Feng-yu Zhou ◽  
Xian-feng Yuan ◽  
Yang Yang ◽  
Zhi-fei Jiang ◽  
Chen-lei Zhou
Keyword(s):  
Mobile Robot ◽  
High Precision ◽  
Visual Localization ◽  
Indoor Mobile Robot

Developing a Self-Localization System for Mobile Robot Using Ultrasonic Sensor Mesh

2016 ◽  
Vol 852 ◽  
pp. 812-818
Author(s):  
Rajneesh Deka ◽  
G. Kalaiarasan ◽  
R. Jegadeeshwaran
Keyword(s):  
Mobile Robot ◽  
Ultrasonic Sensor ◽  
Error Threshold ◽  
Localization System ◽  
Best Value ◽  
Indoor Mobile Robot ◽  
Simple Error ◽  
Position And Orientation

A hybrid self-localization system for indoor mobile robot is proposed which is used to get the pose (position and orientation) of the mobile robot within the ultrasonic mesh area while avoiding the drift caused by the odometry system of the robot. This localization system consist of three subsystem-odometry, IMU and ultrasonic mesh. The IMU system is fitted within the robot chassis. The ultrasonic mesh is made by fixing various ultrasonic trans-receivers along two lines parallel to the x-axis at known locations. The IMU system is used to get the heading of the robot and the ultrasonic mesh is used to get the position of the robot, however the odometry system gives both position and orientation of the robot. A simple error threshold based algorithm is used to select the best value of robot pose from the sub-systems.

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