This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images ...This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.展开更多
This paper combines the least-square method and iteration method to get the fundamental matrix and develops a new evaluation function based on the epipolar geometry. During the iteration, with the evaluation function ...This paper combines the least-square method and iteration method to get the fundamental matrix and develops a new evaluation function based on the epipolar geometry. During the iteration, with the evaluation function as a measurment, the points which bring larger noise are deleted, and the points with smaller noise are retained, thus the precision of our method is increased. The experiment results indicate the new method is precise in calculation, stable in performance and resistant to noise.展开更多
This paper proposes a simple geometrical ray based approach to solve the stereo correspondence problem for the single-lens bi-prism stereovision system. Each image captured using this system can be divided into two su...This paper proposes a simple geometrical ray based approach to solve the stereo correspondence problem for the single-lens bi-prism stereovision system. Each image captured using this system can be divided into two sub-images on the left and right and these sub-images are generated by two virtual cameras which are produced by the bi-prism. This stereovision system is equivalent to the conventional two camera system and the two sub-images captured have disparities which can be used to reconstruct back the 3-dimensional （3D） scene. The stereo correspondence problem of this system will be solved geometrically by applying the epipolar geometry constraint on the generated virtual cameras instead of the real CCD camera. Experiments are conducted to validate the proposed method and the results are compared to the calibration based approach to confirm its accuracy and effectiveness.展开更多
The identification of the correspondences of points of views is an important task. A new feature matching algorithm for weakly calibrated stereo images of curved scenes is proposed, based on mere geometric constraints...The identification of the correspondences of points of views is an important task. A new feature matching algorithm for weakly calibrated stereo images of curved scenes is proposed, based on mere geometric constraints. After initial correspondences are built via the epipolar constraint, many point-to-point image mappings called homographies are set up to predict the matching position for feature points. To refine the predictions and reject false correspondences, four schemes are proposed. Extensive experiments on simulated data as well as on real images of scenes of variant depths show that the proposed method is effective and robust.展开更多
视觉同时定位与地图构建(Simultaneous localization and mapping,SLAM)过程中,动态物体引入的干扰信息会严重影响定位精度。通过剔除动态对象,修复空洞区域解决动态场景下的SLAM问题。采用Mask-RCNN获取语义信息,结合对极几何方法对动...视觉同时定位与地图构建(Simultaneous localization and mapping,SLAM)过程中,动态物体引入的干扰信息会严重影响定位精度。通过剔除动态对象,修复空洞区域解决动态场景下的SLAM问题。采用Mask-RCNN获取语义信息,结合对极几何方法对动态对象进行剔除。使用关键帧像素加权映射的方式对RGB和深度图空洞区域进行逐像素恢复。依据深度图相邻像素相关性使用区域生长算法完善深度信息。在TUM数据集上的实验结果表明,位姿估计精度较ORB-SLAM2平均提高85.26%,较DynaSLAM提高28.54%,在实际场景中进行测试依旧表现良好。展开更多
基金funded by the Center for Unmanned Aircraft Systems(C-UAS)a National Science Foundation Industry/University Cooperative Research Center(I/UCRC)under NSF award Numbers IIP-1161036 and CNS-1650547along with significant contributions from C-UAS industry members。
文摘This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.
基金Supported by the National Science Foundation(69275004)the France-China Advanced Research Program
文摘This paper combines the least-square method and iteration method to get the fundamental matrix and develops a new evaluation function based on the epipolar geometry. During the iteration, with the evaluation function as a measurment, the points which bring larger noise are deleted, and the points with smaller noise are retained, thus the precision of our method is increased. The experiment results indicate the new method is precise in calculation, stable in performance and resistant to noise.
文摘This paper proposes a simple geometrical ray based approach to solve the stereo correspondence problem for the single-lens bi-prism stereovision system. Each image captured using this system can be divided into two sub-images on the left and right and these sub-images are generated by two virtual cameras which are produced by the bi-prism. This stereovision system is equivalent to the conventional two camera system and the two sub-images captured have disparities which can be used to reconstruct back the 3-dimensional （3D） scene. The stereo correspondence problem of this system will be solved geometrically by applying the epipolar geometry constraint on the generated virtual cameras instead of the real CCD camera. Experiments are conducted to validate the proposed method and the results are compared to the calibration based approach to confirm its accuracy and effectiveness.
基金the Ph. D. Programs Foundation of Ministry of Education of China (20040248046).
文摘The identification of the correspondences of points of views is an important task. A new feature matching algorithm for weakly calibrated stereo images of curved scenes is proposed, based on mere geometric constraints. After initial correspondences are built via the epipolar constraint, many point-to-point image mappings called homographies are set up to predict the matching position for feature points. To refine the predictions and reject false correspondences, four schemes are proposed. Extensive experiments on simulated data as well as on real images of scenes of variant depths show that the proposed method is effective and robust.
文摘视觉同时定位与地图构建(Simultaneous localization and mapping,SLAM)过程中,动态物体引入的干扰信息会严重影响定位精度。通过剔除动态对象,修复空洞区域解决动态场景下的SLAM问题。采用Mask-RCNN获取语义信息,结合对极几何方法对动态对象进行剔除。使用关键帧像素加权映射的方式对RGB和深度图空洞区域进行逐像素恢复。依据深度图相邻像素相关性使用区域生长算法完善深度信息。在TUM数据集上的实验结果表明,位姿估计精度较ORB-SLAM2平均提高85.26%,较DynaSLAM提高28.54%,在实际场景中进行测试依旧表现良好。
