Geometric Constraint Image Feature Tracking and Sensor Fusion Technique with Visual-IMU Information

• Main Authors: Tseng, Chin-Yuan, 曾勁源
• Other Authors: Jwu-Sheng Hu
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/97924361167654124183

博士 === 國立交通大學 === 電控工程研究所 === 103 === This dissertation includes three kinds of applications with visual inertial sensor information in 1) image feature tracking, 2) anchor location estimation in wireless sensor networks (WSN), and 3) visual-IMU odometer. In image feature tracking, the epipolar geometry is an important constraint to limit the feature moving area. In this dissertation, the following property is explored: the optical flow vector of the static feature point lies on the epipolar line of cameras with pure translation. For monocular camera motion, the epipolar line then becomes a scan line for feature searching. A constraint feature selection method by using the direction of the epipolar line to filter unstable feature points is proposed. The geometric constraints have no relation to the scene structure or the ratio of the inlier/outlier feature points. To realize the proposed idea, an inertial measurement unit (IMU) is needed to give the rotational information among camera poses. We propose an IMU aided geometric constraint (IGC) feature tracking algorithm. The IGC feature tracking algorithm provides a strength geometric constraint during the feature tracking procedure, and the tracking complexity is . Beyond the geometric constraints, the verification of the tracking result becomes very simple. We propose two kinds of sensor fusion algorithm in anchor node location estimation in wireless sensor network and visual-IMU odometer by using IMU-camera device. In anchor node location estimation, we combined camera trajectory estimation algorithm with a human walking model to realize a scaled visual odometry. Instead of double integration of acceleration, the scale factor from the walking speed estimation uses only the acceleration information of the body. The loosely-coupled approach fuses the RSSI data and attitude of VO to provide an accurate motion trajectory and anchor node locations simultaneously. In visual-IMU odometer, the proposed method uses multi-state constraint Kalman-filter and geometrical constraints of the trifocal tensor and pure translation geometry. The multi-state constraint Kalman filter can fuse the information from the camera and IMU, and the trifocal tensor and pure translation geometric constraints can provide a reliable static feature selection without scene reconstruction. The experiment of the feature tracking includes the latent aperture problem, repeated pattern problem and low texture problem, and also concludes the trajectory estimation results and analysis of locating anchors in WSN and visual-IMU odometer. The experiment results show the effectiveness and robustness of the proposed method.