Abstract
In order to further improve positioning accuracy, this paper proposes an indoor vision/INS integrated mobile robot navigation method using multimodel-based multifrequency Kalman filter. Firstly, to overcome the insufficient accuracy of visual data when a robot turns, a novel multimodel integrated scheme has been investigated for the mobile robots with Mecanum wheels which can make fixed point angled turns. Secondly, a multifrequency Kalman filter has been used to fuse the position information from both the inertial navigation system and the visual navigation system, which overcomes the problem that the filtering period of the integrated navigation system is too long. The proposed multimodel multifrequency Kalman filter gives the root mean square error (RMSE) of 0.0184 m in the direction of east and 0.0977 m in north, respectively. The RMSE of visual navigation system is 0.8925 m in the direction of east and 0.9539 m in north, respectively. Experimental results show that the proposed method is effective.
Highlights
In recent decades, the autonomous capabilities of mobile robots have received increasing attention, and their applications have been widely used [1,2,3,4]
inertial navigation system (INS) measures the navigation information employing the inertial measurement unit (IMU) without external interference, which is composed of a gyroscope, accelerometer, and magnetometer in three directions [11,12,13]. e navigation accuracy of the INS is high in a short period; a disadvantage is that its errors accumulate over time
To overcome insufficient accuracy of visual data when the robot turns, a novel multimodel integrated scheme has been investigated for the mobile robots with Mecanum wheels, which means fixed point turning, that is, the robot position remains unchanged in the process of turning
Summary
The autonomous capabilities of mobile robots have received increasing attention, and their applications have been widely used [1,2,3,4]. Mathematical Problems in Engineering is work tries to improve the localization accuracy of the indoor vision/INS integrated mobile robot navigation using multimodel-based multifrequency Kalman filter. To overcome insufficient accuracy of visual data when the robot turns, a novel multimodel integrated scheme has been investigated for the mobile robots with Mecanum wheels, which means fixed point turning, that is, the robot position remains unchanged in the process of turning. A multifrequency Kalman filter (MKF) is engaged for the fusion of the position given by both the inertial navigation and the visual navigation systems, which overcomes the problem that the filtering period of the integrated navigation system is too long. E indoor integrated navigation scheme employing the vision and INS data is given, while Section 3 introduces the multifrequency Kalman filter.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
