Visual Odometry Algorithms Using Ground Image Sequence from Calibrated Camera and Cooperated Un-Calibrated Cameras
Date Issued
2012
Date
2012
Author(s)
Liang, Chun-An
Abstract
For mobile robots, ego-motion estimation and trajectory reconstruction are two important issues for localizing themselves in the operational environments. Numerous kinds of sensors and techniques are used in robot localization, such as wheel encoder, IMU, GPS, LRF, and visual sensors. Comparing to other sensors, visual sensors could obtain information-rich environment data and usually with low prices, which are good options for robot localization. This thesis proposes two visual odometry methods using ground image sequences. In the first method, image sequence is captured from a well-calibrated monocular camera. Due to the geometrical relationship between ground and camera can be reconstructed by the calibration results, the image scenes could be back projected to the ground and thus get the real-world positions. The proposed visual odometry method with calibrated camera includes mainly three steps. In the first step, the positional correspondences between two consecutive images are established by feature extraction and matching. Then, the extracted features are projected onto the ground plane. Finally, the robot motion is estimated with a Gaussian kernel density voting outlier rejection scheme. For the second method, two un-calibrated cameras mounted on the lateral sides of one robot are used. The intrinsic and extrinsic parameters of cameras are assumed to be unknown and, hence, it is hard to obtain the geometric relationship between image coordinate and world coordinate. To overcome this problem, only a small part of image frame is used to extract the motion quantities for reducing the effect of radial distortion and simplifying the problem as an ordinary wheel odometry problem. The proposed method with un-calibrated cameras includes four steps. In the first step, multiple motion vectors are extracted by block matching. Then, based on the spatial and temporal distribution of motion vectors, unreliable vectors are then determined and deleted. The vectors would be normalized to the desired form to fit the motion model in the next step. Finally, the motion in each frame is calculated and the trajectory is also reconstructed. Both two methods are tested by simulations and real-environment experiments.
Subjects
Visual Odometry
Kernel Density Estimation
Feature Extraction
Feature Matching
Wheel Odometry
Type
thesis
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