Road Constrained Monocular Visual Localization Using Gaussian-Gaussian Cloud Model
Road Constrained Monocular Visual Localization Using Gaussian-Gaussian Cloud Model
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DOI：
10.1109/tits.2017.2685436
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发表时间：
2017-04
影响因子：
8.5
通讯作者：
Shuai Yang;Rui Jiang;Han Wang;S. Ge
中科院分区：
文献类型：
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作者：
Shuai Yang;Rui Jiang;Han Wang;S. Ge
单目视觉里程计在真实的自主导航中的广泛应用受到两个主要挑战：漂移模糊和尺度模糊。在本文中，提出了一种迭代定位框架，全球定位移动的车辆配备了一个单一的摄像头和免费提供的数字地图。受云概念的启发，提出了一种新的高斯-高斯云模型，以统一表示单目视觉里程计中的测量随机性和尺度模糊性。在该模型中，生成云滴的集合。在每个云滴中同时考虑和表示漂移和尺度模糊。为了减少高斯-高斯云中任何下降的测量不确定性，利用开源地图OpenStreetMap的道路约束。首先将地图转换为模板边缘地图，然后执行形状匹配步骤以分配每个云滴的概率，指示下降符合道路约束的程度。提出了一种参数估计方法来减小单目视觉里程计在云量下降时的尺度模糊。KITTI基准数据集和我们自己收集的数据集上的评估表明，所提出的方法的稳定性和准确性。
There are two main challenges, drift and scale ambiguity, restricting monocular visual odometry from an extensive application on real autonomous navigation. In this paper, an iterative localization framework is presented to globally localize a mobile vehicle equipped with a single camera and a freely available digital map. Inspired by the concept of cloud, a new Gaussian–Gaussian Cloud model is proposed to give a unified representation of the measurement randomness and scale ambiguity in monocular visual odometry. In this model, a collection of cloud drops is generated. Both the drift and scale ambiguity are considered and represented simultaneously in each cloud drop. To reduce the measurement uncertainties of any drop in Gaussian–Gaussian Cloud, road constraints from the open source map—OpenStreetMap—are utilized. The map is first converted to a template edge map and a shape matching step is then implemented to assign the probability of each cloud drop, indicating to what degree the drop accords with road constraints. A parameter estimation scheme is used to narrow down the scale ambiguity of monocular visual odometry while resampling cloud drops. Evaluations on the KITTI benchmark data set and our self-collected data set have demonstrated the stability and accuracy of the proposed approach.