Structure from Motion (SfM) techniques are being increasingly used to create 3D maps from images in many domains including environmental monitoring. However, SfM techniques are often confounded in visually repetitive environments as they rely primarily on globally distinct image features. Simultaneous Localization and Mapping (SLAM) techniques offer a potential solution in visually repetitive environments since they use local feature matching, but SLAM approaches work best with wide-angle cameras that are often unsuitable for documenting the environmental system of interest. We resolve this issue by proposing a dual-camera SLAM approach that uses a forward facing wide-angle camera for localization and a downward facing narrower angle, high-resolution camera for documentation. Video frames acquired by the forward facing camera video are processed using a standard SLAM approach providing a trajectory of the imaging system through the environment which is then used to guide the registration of the documentation camera images. Fragmentary maps, initially produced from the documentation camera images via monocular SLAM, are subsequently scaled and aligned with the localization camera trajectory and finally subjected to a global optimization procedure to produce a unified, refined map. An experimental comparison with several state-of-the-art SfM approaches shows the dual-camera SLAM approach to perform better in repetitive environmental systems based on select samples of ground control point markers.
翻译:结构图(SfM)技术正越来越多地用于从许多领域(包括环境监测)的图像中绘制3D地图。然而,SfM技术往往在视觉重复环境中混杂,因为它们主要依赖全球不同的图像特征。同步本地化和绘图(SLAM)技术在视觉重复环境中提供了潜在的解决方案,因为它们使用本地特征匹配,但SLAM方法与往往不适于记录环境系统文件的宽角照相机最为有效。我们提出一个双镜头SLAM方法来解决这一问题,即使用远视宽角照相机进行本地化,向下拍摄较窄角度、高分辨率的文件摄像头。前方摄像片获得的图像框使用标准SLAM方法处理,通过环境提供成像系统的轨迹,然后用于指导文件相机图像的登记。最初通过单轴SLAM制成的断面摄影机图像制作的断裂式地图随后与本地化摄像机轨迹轨迹相配合,最后采用全球优化程序制作统一、精细化的地图。在SLAMSM制模系统上,与若干州级制成的SfM系统进行实验比较。