Pose Estimation and Localization of Using Visual Markers and Their Dense Optical Flow
Date Issued
2015
Date
2015
Author(s)
Yu, Teng-Hsiang
Abstract
Using camera to localize the Unmanned Aerial Vehicle (UAV) is a key technology that has been widely researched in recent decade and has many applications such as indoor exploration, search and rescue, and aerial gripping. Due to the camera only provides color information, the spatial information can be obtained by two steps to localize the camera. The first step is to put the markers in real world with known distances to estimate the transformation between coordinates. Besides, the camera motion can be executed by using the images in different time steps. It can be divided into three stages: correspondence evaluation, motion estimation, local optimization. First, correspondence evaluation is used to find some distinct features with their special characteristics to match images. Second, the corresponding pairs are used to estimate the camera motion. Third, the optimization method is used to correct the localization result. In this these, the first localization step is implemented by designing the markers with particular colors for the detection method in the image plane, and setting them with known distance in the real world. The transformation between image and spatial coordinates is used to evaluate the relationship between the camera and the spatial coordinates. Otherwise, by discussing the localization and calibration results, the setting of principle point at the center of the image plane can make the localization result is more accurate. Furthermore, by the sensitivity analysis, the localization result is affected by the detection error of features. The other step in this thesis is using the dense optical flow to describe the correspondence between two images in different time steps. To reduce the information, the flow vectors are segmented into non-overlapping blocks. Each block can be classified into the group according to the mean angle of flow vectors. Moreover, an edge filter is proposed to obtain the reliable blocks. Due to the displacement of the static scene in the image is reverse to the motion direction of the camera, then the outliers of optical flow reveal in the image plane about the direction of the camera motion. The distribution of each group is used to estimate the vertical motion of the camera. In the ideal experiment, the motion estimation result with the edge filter is more accurate. In addition, the roll rotation of UAV makes the spiral pattern of optical flow. Then, the Hough circle transformation is used to obtain the circular subsets. The linear and exponential weighting functions are designed by the standard deviations of the subsets. The estimation result of roll by using the exponential weighting function is more accurate than the result by using the linear weighting function. In order to implement these two steps in real-flight scenario, the validation analysis is used to evaluate the flow map is reliable or not. The analysis result shows that the method can effectively judge the textureless region in the image plane. However, the estimation results in real-flight experiment show that the outliers of optical flow do not reveal as expectation and the pattern of optical flow does not involve the effect of horizontal movement of the quadrotor.
Subjects
Camera localization
camera calibration
sensitivity analysis
dense optical flow
validation analysis
motion estimation
Type
thesis
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