Abstract
As indoor mobile navigation suffers from low positioning accuracy and accumulation error, we carried out research into an integrated location system for a robot based on Kinect and an Inertial Measurement Unit (IMU). In this paper, the close-range stereo images are used to calculate the attitude information and the translation amount of the adjacent positions of the robot by means of the absolute orientation algorithm, for improving the calculation accuracy of the robot’s movement. Relying on the Kinect visual measurement and the strap-down IMU devices, we also use Kalman filtering to obtain the errors of the position and attitude outputs, in order to seek the optimal estimation and correct the errors. Experimental results show that the proposed method is able to improve the positioning accuracy and stability of the indoor mobile robot.
Highlights
In mobile robot navigation, stable and reliable positioning results are the key prerequisite for planning paths
The visual positioning results and the inertial navigation system (INS) data are fused by Kalman filter algorithm to improve the self-positioning accuracy of the indoor mobile robot
0.7954 m to 0.6078 m, and the relative visual acuity increased by 3.4%. These results show that the cumulative positioning error of the short-distance visual range is very small, while the positioning accuracy is high
Summary
Stable and reliable positioning results are the key prerequisite for planning paths. There is a positioning error in the visualization positioning method due to the uncertain location estimation of the spatial feature points. Feature point extraction and matching algorithms may fail in purely visual autonomous navigation due to insufficient or excessive external light. IMU, which has simple, real-time and all-weather completely autonomous navigation functionality, can compensate for visual measurement errors in accuracy and speed [13], it still has many serious problems, such as accumulated positioning error. This paper investigated indoor mobile robot integrated navigation and positioning based on Kinect and IMU. The visual positioning results and the inertial navigation system (INS) data are fused by Kalman filter algorithm to improve the self-positioning accuracy of the indoor mobile robot.
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