Strap-down inertial navigation system applied in estimating the track of mobile robot based on multiple-sensor

2013 ◽  
Author(s):  
Zhang Qingxin ◽  
Wang Luping ◽  
Zhou Shuaishuai
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Multiple Sensor

scholarly journals Analysis of control and correction options of mobile robot trajectory by an inertial navigation system

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875516 ◽  
Author(s):  
Elena Pivarčiová ◽  
Pavol Božek ◽  
Yuri Turygin ◽  
Ivan Zajačko ◽  
Aleksey Shchenyatsky ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Control Systems ◽  
Navigation System ◽  
Reference System ◽  
Inertial Navigation System ◽  
Industrial Robot ◽  
Inertial Navigation ◽  
Control Device ◽  
Robot Trajectory ◽  
Detailed Application

The article deals with the research of the supplementation of industrial robot effector trajectory’s control systems by an inertial navigation system. The method of reverse validation and location of an object in a navigated reference system does not require additional calibration. The goal of the research is to verify the assumption that it is possible to control and correct the programmed mobile robot trajectory by implementing an inertial navigation system even in a case when the inertial navigation system is used as the only trajectory control device. The data obtained are processed by the proposed and detailed application.

scholarly journals Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2922 ◽  
Author(s):  
Dinh Van Nam ◽  
Kim Gon-Woo
Keyword(s):  
Mobile Robot ◽  
State Estimation ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dynamic Environments ◽  
Indoor Environments ◽  
Outlier Removal ◽  
Multi Stage ◽  
Dynamic Objects

Robotic mapping and odometry are the primary competencies of a navigation system for an autonomous mobile robot. However, the state estimation of the robot typically mixes with a drift over time, and its accuracy is degraded critically when using only proprioceptive sensors in indoor environments. Besides, the accuracy of an ego-motion estimated state is severely diminished in dynamic environments because of the influences of both the dynamic objects and light reflection. To this end, the multi-sensor fusion technique is employed to bound the navigation error by adopting the complementary nature of the Inertial Measurement Unit (IMU) and the bearing information of the camera. In this paper, we propose a robust tightly-coupled Visual-Inertial Navigation System (VINS) based on multi-stage outlier removal using the Multi-State Constraint Kalman Filter (MSCKF) framework. First, an efficient and lightweight VINS algorithm is developed for the robust state estimation of a mobile robot by practicing a stereo camera and an IMU towards dynamic indoor environments. Furthermore, we propose strategies to deal with the impacts of dynamic objects by using multi-stage outlier removal based on the feedback information of estimated states. The proposed VINS is implemented and validated through public datasets. In addition, we develop a sensor system and evaluate the VINS algorithm in the dynamic indoor environment with different scenarios. The experimental results show better performance in terms of robustness and accuracy with low computation complexity as compared to state-of-the-art approaches.

Study on Method of Autonomous Mobile Robot Integrated Navigation Based on SLAM

2012 ◽  
Vol 263-266 ◽  
pp. 1290-1297 ◽  
Author(s):  
Jing Zeng ◽  
Xiao Song Guo ◽  
Guo Liang Zhang
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
The Self ◽  
Autonomous Mobile Robot ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Unknown Environment ◽  
Navigation Scheme

Aiming at the problem of navigation in unknown environment of autonomous mobile robot, the self-made mobile robot was taken as research object. The integrated navigation technology was adapted to integrate the SLAM navigation system, DR navigation system and MEMS micro inertial navigation system possessed by the studied robot to construct the SLAM/DR/MEMS integrated navigation system of mobile robot. The experiments were carried to test the method, and the results show that the precision of navigation and location of mobile robot was developed by the integrated navigation scheme.

scholarly journals Mobile robot positioning algorithm based on Kalman filtering method in network environment

2020 ◽  
Vol 327 ◽  
pp. 03005
Author(s):  
Shuang Zhang
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Relative Positioning ◽  
Positioning Accuracy ◽  
Reference Information ◽  
Absolute Positioning ◽  
Positioning Method

Positioning is the basic link in a multi-mobile robot control system, and is also a problem that must be solved before completing a specified task. The positioning method can be generally divided into relative positioning and absolute positioning. Absolute positioning method refers to that the robot calculates its current position by acquiring the reference information of some known positions in the outside world, calculating the relationship between itself and the reference information. Absolute positioning generally adopts methods based on beacons, environment map matching, and visual positioning. The relative positioning method mainly uses the inertial navigation system INS. The inertial navigation system directly fixes the inertial measurement unit composed of the gyroscope and the accelerometer to the target device, and uses the inertial devices such as the gyroscope and the accelerometer to measure the triaxial angular velocity and The three-axis acceleration information is measured and integrated, and the mobile robot coordinates are updated in real time. Combined with the initial inertial information of the target device, navigation information such as the attitude, speed, and position of the target device is obtained through integral operation [1-2]. The inertial navigation system does not depend on external information when it is working, and is not easily damaged by interference. As an autonomous navigation system, it has the advantages of high data update rate and high short-term positioning accuracy [3]. However, under the long-term operation of inertial navigation, due to the cumulative error of integration, the positioning accuracy is seriously degraded, so it is necessary to seek an external positioning method to correct its position information [4]

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