Abstract: Due to the limit of depth information range of RGB-D camera and their vulnerability to noise, in order to improve the accuracy of the RGB-D visual odometry, a method of visual odometry with hybrid motion estimation model based on 3D-3D and 3D-2D is proposed. Firstly, using the 3D-3D model based on RICP algorithm（RANSAC-ICP）, combined with the 3D-2D motion model, the 2D feature points with missing depth information are added, which makes full use of the image information and improves the matching accuracy. Secondly, the map information of the key frame and the previous frame is considered to estimate iteratively, which increases the number of matching points and provides more constraint information. Finally, on the basis of the hybrid estimation method, the SBA method is combined to optimize the pose estimation results, which achieves high localization accuracy and small accumulation error. In this paper, experiments are performed on a Kinect-based mobile platform. The comprehensive offline datasets and online experimental tests show that this method not only meets the requirement of real-time, but also improves the accuracy of the mobile locali-zation effectively.

Key words: visual vdometry, Oriented FAST and Rotated BRIEF（ORB）, hybrid motion estimation, Sparse Bundle Adjustment（SBA）, Kinect

摘要： 由于RGB-D相机深度信息范围有限且易受噪声影响，为了提高其视觉里程计的精度，提出一种基于混合3D-3D和3D-2D运动估计方法的视觉里程计。使用基于RICP算法（RANSAC-ICP）的3D-3D模型，并结合3D-2D运动模型，将深度信息缺失的二维特征点添加到估计方法中，充分利用了图像信息，提高了匹配准确率。综合考虑了关键帧和前一帧的地图信息进行迭代估计，增加了匹配点对数量，提供了更多约束信息。在该混合运动估计方法的基础上，结合稀疏光束平差法SBA对位姿估计结果进行优化，达到定位精度高、积累误差小的效果。在基于Kinect相机的移动平台上进行了验证，结合离线和在线实验表明，该方法满足实时性同时有效地提高了定位精度。

关键词: 视觉里程计, ORB, 混合运动估计, 稀疏光束平差法, Kinect