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：本文提出一种面向水下车辆机械手系统(UVMS)的新型视觉特征场景建图方法，特别强调在自然海床环境下的鲁棒建图能力。我们的方法采用基于GPU加速的SIFT特征，在图形优化框架下构建特征地图。地图尺度通过车载立体摄像机特征进行约束，同时利用机械手系统的动态定位能力，将腕部鱼眼摄像机特征融合至地图中，从而突破车载摄像机有限视角的限制。该方法在哥斯达黎加大陆架边缘自然深海环境中的UVMS采集的挑战性图像序列上进行了验证，并在浅礁调查数据集上测试了仅使用立体相机的模式。实验结果表明，我们的系统具有高精度特性，且适用于多样化的自然海床环境操作。此外，我们还将这些数据集公开以供研究使用。