相机位姿估计是 SLAM 系统的关键环节，影响着整个 SLAM 系统的精度和效率． 针对 SLAM 中相机位姿估计存在的问题，提出了一种改进的相机位姿估计方法． 该方法的 主要思路是将特征点法和直接法结合起来，以此来提升特征点数量不足时相机位姿估计 的精度和鲁棒性． 首先，提出了一个将相机运动模型和图像划分相结合的特征匹配算法， 该算法在保证匹配速度的同时，提高了特征匹配的精度与数量． 其次，在特征点的基础上， 通过引入光度信息，提出了表观形状加权融合的相机位姿估计方法，该方法在缺乏特征点 时依然可以稳定工作． 最后，基于优选的关键帧，实现了局部与全局融合的相机位姿优化， 其中局部优化通过构建局部关键帧共视关系实现;全局优化通过基于闭环检测构建的位 姿图来实现． 为验证上述位姿优化方法的性能，构建了基于该方法的 SLAM 系统，并在当 前流行的场景图像数据集上进行了重建实验，重建结果验证了本文方法的有效性．

Camera pose estimation is a key step in SLAM system，which affects the accuracy and efficiency of the whole SLAM system． At present，there are two main methods to estimate the pose of camera，namely，the feature point method and the direct method． The accuracy of feature point method depends on the number of feature points and the correctness of feature matching． When enough feature points cannot be extracted in the scene，the position and posture of the camera cannot be estimated accurately． The direct method estimates the position of the camera by the pixel’s photometric error，and does not need to extract the feature points． Therefore，the direct method can still estimate the position and pose of the camera more accurately when the feature point method is unable to work． But the direct method assumes the luminosity invariance，so the accuracy of the result is not as good as that of the characteristic point method． Aiming at the problems of camera pose estimation in SLAM，an improved camera pose estimation method is proposed in this paper． The main idea of this method is to combine the feature point method with the direct method to overcome the estimation of the position and posture of the camera when the feature point is lacking，and to improve the accuracy and robustness of the position and posture of the camera． In particular，first， a feature matching algorithm which combines camera motion model with image division is proposed． The algorithm improves the accuracy and quantity of feature matching while guaranteeing the matching speed． Secondly，on the basis of the feature points，by introducing the photometric information，an apparent shape weighted fusion method is proposed to estimate the position and posture of the camera． This method can still work steadily when the feature points are lacking． Finally，on the basis of the preferred key frame，the local and global fusion of camera pose opti- mization is realized，in which the local optimization is realized by constructing the common view relationship of the local key frame，and the global optimization is realized by the pose graph based on the closed loop detection． In or- der to verify the performance of the pose optimization method，a SLAM system based on this method is constructed， and the reconstruction experiments are carried out on the current popular scene image data set． The reconstruction results verify the effectiveness of this method．