水下车辆机械臂系统的混合视觉SLAM
Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems
AI总结：
本文提出一种基于视觉特征的UVMS混合SLAM方法，通过融合车载立体相机和腕部鱼眼相机特征扩展地图，在自然海底环境中表现出高精度。
摘要：
本文提出了一种新颖的基于视觉特征的水下车辆机械臂系统（UVMS）场景建图方法，特别强调在自然海底环境中的鲁棒建图。该方法在图优化框架中使用GPU加速的SIFT特征构建特征地图。地图尺度由车载立体相机的特征约束，我们利用机械臂系统的动态定位能力，将腕部鱼眼相机的特征融合到地图中，以将地图扩展到车载相机有限视角之外。我们的混合SLAM方法在哥斯达黎加大陆架边缘的自然深海海底环境中使用UVMS收集的具有挑战性的图像序列上进行了评估，我们还在浅礁调查数据集上评估了纯立体模式。在这些数据集上的结果证明了我们系统的高精度及其在多样化和自然海底环境中运行的适用性。我们还贡献这些数据集供公众使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
