Wang Ding, Wang Junhua, Tian Yuhan, Hu Kai, Xu Min
Opt Express. 2022 Jan 17;30(2):1099-1113. doi: 10.1364/OE.447893.
Simultaneous localization and mapping (SLAM) is widely used in autonomous driving and intelligent robot positioning and navigation. In order to overcome the defects of traditional visual SLAM in rapid motion and bidirectional loop detection, we present a feature-based PAL-SLAM system for a panoramic-annular-lens (PAL) camera in this paper. We use a mask to extract and match features in the annular effective area of the images. A PAL-camera model, based on precise calibration, is used to transform the matched features onto a unit vector for subsequent processing, and a prominent inlier-checking metric is designed as an epipolar constraint in the initialization. After testing on large-scale indoor and outdoor PAL image dataset sequences, comprising of more than 12,000 images, the accuracy of PAL-SLAM is measured as typically below 1 cm. This result holds consistent in conditions when the camera rotates rapidly, or the Global Navigation Satellite System (GNSS) signals are blocked. PAL-SLAM can also detect unidirectional and bidirectional loop closures. Hence it can be used as a supplement or alternative to expensive commercial navigation systems, especially in urban environments where there are many signal obstructions such as buildings and bridges.
同步定位与地图构建（SLAM）在自动驾驶以及智能机器人定位与导航中有着广泛应用。为克服传统视觉SLAM在快速运动和双向回环检测方面的缺陷，本文提出了一种基于特征的全景环形镜头（PAL）相机的PAL-SLAM系统。我们使用一个掩码在图像的环形有效区域提取和匹配特征。基于精确校准的PAL相机模型用于将匹配的特征转换到单位向量上以便后续处理，并且设计了一种突出的内点检查度量作为初始化中的极线约束。在由超过12000张图像组成的大规模室内和室外PAL图像数据集序列上进行测试后，PAL-SLAM的精度通常测量为低于1厘米。在相机快速旋转或全球导航卫星系统（GNSS）信号被阻挡的情况下，该结果保持一致。PAL-SLAM还可以检测单向和双向回环闭合。因此，它可以用作昂贵商业导航系统的补充或替代方案，特别是在存在许多诸如建筑物和桥梁等信号障碍物的城市环境中。
