中文摘要：

针对复杂环境下机器人的同时定位与地图构建（SLAM）存在实时性与鲁棒性下降等问题,将一种基于ORB特征点的关键帧闭环检测匹配算法应用到定位与地图构建中。研究并分析了特征点提取与描述符建立、帧间配准、位姿变换估计以及闭环检测对SLAM系统的影响,建立了关键帧闭环匹配算法和SLAM实时性与鲁棒性之间的关系,提出了一种基于ORB关键帧匹配算法的SLAM方法。运用改进ORB算法加快了图像特征点提取与描述符建立速度;结合相机模型与深度信息,可将二维特征图像转换为三维彩色点云;通过随机采样一致性（RANSAC）与最近迭代点（ICP）相结合的改进RANSAC-ICP算法,实现了机器人在初始配准不确定条件下的位姿估计;使用Key Frame的词袋闭环检测算法,减少了地图的冗余结构,生成了具有一致性的地图;通过特征点匹配速度与绝对轨迹误差的均方根值对SLAM系统的实时性与鲁棒性进行了评价。基于标准测试集数据集的实验结果表明,ORB关键帧匹配算法能够有效提高SLAM系统建图速度与稳定性。

英文摘要：

Aiming at the problems of the decline in real-time and robust performance of robot simultaneous localization and mapping（SLAM） in a complex environment, an optimization frame of SLAM based on improved ORB（oriented FAST and rotated BRIEF） keyframe detection and matching algorithm was proposed. After the analysis of keypoint detection, frame matching, motion estimation and loop-closure detection algorithm, the relationship between keyframe loop-closure matching algorithm and SLAM system was established. An improved ORB algo- rithm was adopted to implement the fast and efficient matching between two adjacent RGB frames. Combined camera perspective projection model with dense frames, the 3D color point clouds can be transformed from adjacent matched 2D frames. Then the relative pose between the adjacent frames was computed by improved RANSAC-ICP algorithm, which can solve the mobile robot precise localization problem. The key- frame Bag-of-Word algorithm was the basis of loop-closure detection, which can improve the mapping speed and consistency. The purpose of closure detection was to reduce redundant model structure and generate a map with consistency. The real-time and robust performance were e- valuated by matching speed and root-mean-square （RMSE） of the absolute trajectory error （ATE）. The Results base on standard testing in- dicate that the robot can build a precise environment model where the robot can localize itself real-time and robustly.