光学学报, 2017, 37 (11): 1115001, 网络出版: 2018-09-07   

动态场景下基于运动物体检测的立体视觉里程计 下载: 957次

Stereo Visual Odometry Based on Motion Object Detection in the Dynamic Scene
作者单位
火箭军工程大学控制科学与工程系, 陕西 西安 710025
摘要
为了提升动态场景中视觉里程计的稳健性和精确度,提出一种基于运动物体检测的立体视觉里程计算法。首先,建立考虑相机位姿的场景流计算模型,用于表示物体的运动矢量。其次,提出构造虚拟地图点的方法,一方面结合场景流进行运动物体检测,另一方面使运动物体在图像中占比较大时仍有足够匹配点对用于位姿估计。最后,通过局部地图点及虚拟地图点与当前帧特征点的匹配结果,构建考虑虚拟点的非线性优化模型进行相机位姿估计,既保证静态地图点不与运动物体的特征点形成错误匹配,又避免因有效匹配点对过少而导致视觉里程计失效。数据集实验和实际场景在线实验结果表明,本文算法提升了视觉里程计在动态场景中的稳健性和精确度。
Abstract
In order to improve the robustness and accuracy of the visual odometry in the dynamic scene, a stereo visual odometry based on moving object detection is proposed. Firstly, a scene flow calculation model considering the camera pose is established to represent the motion vector of the objects. Secondly, the method of constructing virtual map points is proposed. On the one hand, the motion object detection can be complied according to the virtual map points and the scene flow, on the other hand, the virtual map points ensure that there are still enough matched point pairs to estimate the pose when the proportion of the moving objects in the image is too large. Finally, the feature points in the current frame will be matched with the local map points and the virtual map points, and according to the matching results, the nonlinear optimization model considering the virtual points is constructed to estimate the camera pose. It can not only ensure that the static map points do not match with the feature points of the motion object, but also avoid the failure of the visual odometry when the effective point pairs are too few. The results of the dataset experiment and the online experiment in the actual scene show that the proposed method improves the robustness and accuracy of the visual odometry in the dynamic scene.

林志林, 张国良, 姚二亮, 徐慧. 动态场景下基于运动物体检测的立体视觉里程计[J]. 光学学报, 2017, 37(11): 1115001. Zhilin Lin, Guoliang Zhang, Erliang Yao, Hui Xu. Stereo Visual Odometry Based on Motion Object Detection in the Dynamic Scene[J]. Acta Optica Sinica, 2017, 37(11): 1115001.

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