Pose estimation of a mobile robot
- 1. 1Kyungpook National University Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter Mary B. Alatise and Gerhard P. Hancke SENSORS JOURNAL, September 21, 2017
- 2. 2Kyungpook National University Motivation The objective of this paper is to estimate the pose of a device. This paper presents a fusion of IMU sensor and vision to determine the accurate position of mobile robot .
- 3. 3Kyungpook National University Why we need fusion of IMU sensor and vision IMU sensor accumulated drift Sensor Calibration Vision illumination visual occlusion Blurred features under fast and unpredicted motions. Field of view of the camera Use the complementary features of IMU sensor and vison based methods. Advantages : Implementation low cost fast computation improved accuracy
- 4. 4Kyungpook National University Proposed Method Overview of the stages of IMU and Vision fusion
- 5. 5Kyungpook National University Hardware used for the experiment: a) Arduino 101 microcontroller b) LinkSprite camera (c) 4WD robot platform
- 6. 6Kyungpook National University Results and Discussion Simulated Results of Object Detection and Recognition in an Image Proposed object (query image) Training image (RGB image) Convert the image from RGB to grayscale Removal of lens distortion Image including Outliers and inliers Image with inliers Images with display box around the recognized object
- 7. 7Kyungpook National University Simulated Results of Object Analysis of Experimental Results Euler angles from IMU. Roll: red; pitch: green; yaw: blue. The figure shows the robot travelling on a flat surface. At 49s, roll and pitch angles maintained a close to zero angle until there was a change in direction. At the point where the robot turned 90 degree to the right, the yaw angle was 91.25 degrees. The maximum values obtained for pitch and roll angles are 15 degree and 18 degree. From the experiment results, it can be conclude that Euler angles are a good choice for the experiment performed because the pitch angle did not attain ±90 degree to cause Gimbal lock.
- 8. 8Kyungpook National University Orientation results for fused sensors When the robot made a 90 degree right turn, Yaw value for IMU was 91.25 degree Yaw value for vision was 88 degree Pitch value 1.2 degree Roll value 4 degree From the results, it can be conclude that the proposed method reduced the accumulated errors and drifts. Roll angles Pitch angles Yaw angles
- 9. 9Kyungpook National University comparison of the three directions of the mobile robot taken from vision method Experimental position in XYZ directions from vision data. The figure shows a distinctive estimation of position of the mobile robot. The position estimation based on the reference object in the image is relative to the position of the mobile robot and the world coordinate , with the median vector of the planar object for Z-axis close to 1 and -1. From the experimental position result, it can be conclude that the SURC and RANSAC algorithms combination can be used for the accurate position estimation.
- 10. 10Kyungpook National University Ground truth data The ground truth data was collected with the use of external camera placed in the environment of experiment. The camera was placed on a flat terrain with the mobile robot with a distance of 4.90 m Ground truth system based on a camera
- 11. 11Kyungpook National University Comparison Trajectory in the XY plane Position corresponding to the trajectory
- 12. 12Kyungpook National University Performance: Accuracy The accuracy of proposed method is evaluated by root mean square error (RMSE) Figures show the difference between the ground truth and proposed method. Maximum error value for position is 0.145m Maximum error value for orientation is 0.95 degree. RMSE for position RMSE for orientation
- 13. 13Kyungpook National University RMSE of position and orientation Position error slightly increases with increase in time. Both pitch and yaw error angles decreases as time increases. Roll error was gradually increasing from the start time to about 80 s and later decreases.
- 14. 14Kyungpook National University CONCLUSION Computer vision and IMU sensor fusion. For object recognition SURF and RANSAC algorithms were used to detect and match features in images. Experimental results show that proposed method gives better performance as compared to results from individual methods. The proposed method gives high position accuracy. The error values from proposed method are reasonable for indoor localization.