研究目的
Investigating the use of oriented features to reduce the number of point correspondences required for relative camera pose estimation.
研究成果
The four- and seven-point algorithms for relative camera pose estimation using oriented features significantly reduce computation time compared to traditional methods, with similar or improved accuracy in most cases. The four-point algorithm is 55% faster than the five-point algorithm, and the seven-point algorithm provides a 43% speed improvement over the eight-point algorithm on the DTU data set. These methods exploit feature orientation information already available in most structure-from-motion systems, without requiring additional sensors or parameters.
研究不足
The proposed methods are most sensitive to errors in cases of small rotation combined with translation along the camera axis, which is common in vehicle navigation tasks. The accuracy and robustness of the methods depend on the reliability of feature orientation estimates.
1:Experimental Design and Method Selection:
The study compares the performance of four- and seven-point algorithms against traditional five- and eight-point algorithms for relative camera pose estimation using oriented features.
2:Sample Selection and Data Sources:
The DTU multi-view stereo data set, Middlebury multi-view stereo evaluation, and KITTI vision benchmark images were used for evaluation.
3:List of Experimental Equipment and Materials:
SIFT features for detection and matching, OpenCV
4:0 for feature detection and matching, and Eigen 4 for linear algebra support. Experimental Procedures and Operational Workflow:
Feature correspondences were used to estimate relative pose with a RANSAC approach to remove outliers. The number of iterations, time, and accuracy of pose estimation were compared across algorithms.
5:Data Analysis Methods:
The accuracy of pose estimation was measured by the angles between estimated and ground truth translation vectors and rotation matrices. Effect sizes were calculated to compare the performance of different algorithms.
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