We present a vision‐based algorithm that identifies the boundary separating water from land in a river environment containing specular reflections. Our approach relies on the law of reflection. Assuming the surface of water behaves like a horizontal mirror, the border separating land from water corresponds to the border separating three‐dimensional (3D) data which are either above or below the surface of water. We detect a river by identifying this border in a stereo camera. We start by demonstrating how to robustly estimate the normal and height of the water's surface with respect to a stereo camera. Then, we segment water from land by identifying the boundary separating dense 3D stereo data which are either above or below the water's surface. We explicitly show how to find this boundary by formulating and solving a graph‐based optimization problem using dense 3D stereo data near the shoreline and Dijkstra's algorithm. With the border of water identified, we validate the proposed river boundary detection algorithm by applying it to a chronologically sequential video sequence obtained from the visual‐inertial canoe data set. The intended purpose of the proposed river segmentation algorithm is to be used as a front‐end object recognition module for solving the simultaneous localization and mapping (SLAM) problem; therefore, using the extracted river boundary, we apply the recently developed visual‐inertial Curve SLAM algorithm to localize a canoe and create a sparse map that recovers the outline, shape, and dimensions of the shoreline of a river.

中文翻译：

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用于自主地面车辆定位和河流边界映射的河流分割

我们提出了一种基于视觉的算法，该算法可识别在包含镜面反射的河流环境中将水与陆地分开的边界。我们的方法依赖于反射定律。假设水面表现得像一面水平镜，陆地与水面的边界对应于水面以上或水面以下的三维（3D）数据的边界分离。我们通过在立体相机中识别这条边界来检测河流。我们首先演示如何相对于立体相机稳健地估计水面的法线和高度。然后，我们通过识别分隔水面上方或下方的密集 3D 立体数据的边界来将水与陆地分开。我们明确地展示了如何通过使用海岸线附近的密集 3D 立体数据和 Dijkstra 算法制定和解决基于图的优化问题来找到这个边界。识别出水的边界后，我们通过将其应用于从视觉惯性独木舟数据集中获得的按时间顺序排列的视频序列来验证所提出的河流边界检测算法。所提出的河流分割算法的预期目的是用作解决同时定位和映射（SLAM）问题的前端对象识别模块；因此，使用提取的河流边界，我们应用最近开发的视觉惯性曲线 SLAM 算法来定位独木舟并创建稀疏地图，以恢复河流海岸线的轮廓、形状和尺寸。