Experimental study on sensor fusion to improve real time indoor localization of a mobile robot

2011 ◽  
Author(s):  
Jason Zhou ◽  
Loulin Huang
Keyword(s):  
Experimental Study ◽  
Mobile Robot ◽  
Real Time ◽  
Sensor Fusion ◽  
Indoor Localization

scholarly journals High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xianmin Li ◽  
Zihong Yan ◽  
Linyi Huang ◽  
Shihuan Chen ◽  
Manxi Liu
Keyword(s):  
Mobile Robot ◽  
Visible Light ◽  
Real Time ◽  
Indoor Localization ◽  
Visible Light Communication ◽  
High Accuracy ◽  
Market Potential ◽  
Location Based Services ◽  
Positioning System ◽  
Robot Positioning

For mobile robots and location-based services, precise and real-time positioning is one of the most basic capability, and low-cost positioning solutions are increasingly in demand and have broad market potential. In this paper, we innovatively design a high-accuracy and real-time indoor localization system based on visible light positioning (VLP) and mobile robot. First of all, we design smart LED lamps with VLC and Bluetooth control functions for positioning. The design of LED lamps includes hardware design and Bluetooth control. Furthermore, founded on the loose coupling characteristics of ROS (Robot Operator System), we design a VLP-based robot system with VLP information transmitted by designed LED, dynamic tracking algorithm of high robustness, LED-ID recognition algorithm, and triple-light positioning algorithm. We implemented the VLP-based robot positioning system on ROS in an office equipped with the designed LED lamps, which can realize cm-level positioning accuracy of 3.231 cm and support the moving speed up to 20 km/h approximately. This paper pushes forward the development of VLP application in indoor robots, showing the great potential of VLP for indoor robot positioning.

Obstacles Perception for Serpentine Robot Based on Optical Flow Modules and Multi-Sensor Fusion

2011 ◽  
Vol 55-57 ◽  
pp. 1699-1704
Author(s):  
Jie Zhao ◽  
Zhen Feng Han ◽  
Gang Feng Liu ◽  
Yong Min Yang
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Real Time ◽  
Sensor Fusion ◽  
Image Sequence ◽  
Higher Order ◽  
Experimental Results ◽  
New Approach ◽  
Environment Perception ◽  
Indoor And Outdoor

To move efficiently in an unknown environment, a mobile robot must use observations taken by various sensors to detect obstacles. This paper describes a new approach to detect obstacles for serpentine robot. It captures the image sequence and analyzed the optical flow modules to estimate the deepness of the scene. This avoids one or higher order differential in the traditional optical flow calculation. The data of ultrasonic sensor and attitude transducer sensor are fused into the algorithm to improve the real-time capability and the robustness. The detecting results are presented by fuzzy diagrams which is concise and convenient. Indoor and outdoor experimental results demonstrate that this method can provide useful and comprehensive environment perception for the robot.

Real-time Control for Mobile Robot Considering Environmental Changes

2007 ◽  
Vol 73 (12) ◽  
pp. 1369-1374
Author(s):  
Hiromi SATO ◽  
Yuichiro MORIKUNI ◽  
Kiyotaka KATO
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Environmental Changes ◽  
Real Time Control ◽  
Time Control

Real-time road edge following for mobile robot navigation

1990 ◽  
Vol 2 (1) ◽  
pp. 35 ◽  
Author(s):  
R.A. Lotufo ◽  
A.D. Morgan ◽  
E.L. Dagless ◽  
D.J. Milford ◽  
J.F. Morrissey ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Robot Navigation ◽  
Mobile Robot Navigation

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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