考虑多位姿估计约束的双目视觉里程计

为了提升复杂环境中双目视觉里程计的精度,提出一种考虑多位姿估计约束的双目视觉里程计方法.首先,分别建立匹配深度已知点与深度未知点的数学模型,将深度未知点引入2D-2D位姿估计模型,从而充分利用图像信息;然后,基于关键帧地图点改进3D-2D位姿估计模型,并结合当前帧地图点更新关键帧地图点,从而增加匹配点对数,提高位姿估计精度;最后,根据改进的2D-2D及3D-2D位姿估计模型,建立多位姿估计约束位姿估计模型,结合局部光束平差法对位姿估计进行局部优化,达到定位精度高且累积误差小的效果.数据集实验和实际场景在线实验表明,所提出方法满足实时定位要求,且有效地提高了自主定位精度.

Stereo visiual odometry with multi-pose estimation constraints

In order to improve the accuracy of the stereo visual odometry in complex environment, a method of stereo visiual odometry with multi-pose estimation constraints is proposed. Firstly, the mathematical models of matching points with the known depth and the unknown depth are established respectively, and then an improved 2D-2D pose estimation model considering the depth of unknown points is proposed, so that the image information can be used fully. Then, the 3D-2D pose estimation model is improved based on the keyframe mappoints, and the keyframe mappoints are updated according to the mappoints corresponding to the current frame. Therefore, the number of matched features can be more and the accuracy can be increased. Finally, according to the improved 2D-2D and 3D-2D pose estimation model, the pose estimation model with multi-pose estimation constraints is eatablished. Then the local bundle adjustment method is applied to optimize the estimated pose, so that the accuracy will be high and the cumulative error will be small. The experiments based on the datasets and the online experiment based on the actual scene show that, this method not only can meet the requirements of real-time location, but also can improve the accuracy of the mobile robot autonomous localization effectively.