This paper presents a novel visual feature based scene mapping method for underwater vehicle manipulator systems (UVMSs), with specific emphasis on robust mapping in natural seafloor environments. Our method uses GPU accelerated SIFT features in a graph optimization framework to build a feature map. The map scale is constrained by features from a vehicle mounted stereo camera, and we exploit the dynamic positioning capability of the manipulator system by fusing features from a wrist mounted fisheye camera into the map to extend it beyond the limited viewpoint of the vehicle mounted cameras. Our hybrid SLAM method is evaluated on challenging image sequences collected with a UVMS in natural deep seafloor environments of the Costa Rican continental shelf margin, and we also evaluate the stereo only mode on a shallow reef survey dataset. Results on these datasets demonstrate the high accuracy of our system and suitability for operating in diverse and natural seafloor environments. We also contribute these datasets for public use.
翻译:本文提出了一种新型的基于视觉特征的场景映射方法,用于水下机器人操纵系统(UVMSs),并特别强调了在自然海床环境中进行鲁棒映射的重要性。我们的方法使用GPU加速的SIFT特征在图形优化框架中构建特征地图。地图尺度受到车载立体相机的特征约束,并通过融合手腕配备的鱼眼摄像头的特征来拓展地图,利用操纵系统的动态定位能力。我们的混合SLAM方法在哥斯达黎加大陆架边缘的自然深海床环境中采集的挑战性图像序列上进行评估,并在一个浅水礁环境数据集上评估仅使用立体相机的模式。这些数据集的结果表明了我们系统的高精度和适用性,适用于在各种自然的海底环境中操作。我们也为公众提供了这些数据集。