We propose a low-cost laboratory platform for development and validation of underwater mapping techniques, using the BlueROV2 Remotely Operated Vehicle (ROV). Both the ROV and the objects to be mapped are placed in a pool that is imaged via an overhead camera. In our prototype mapping application, the ROV's pose is found using extended Kalman filtering on measurements from the overhead camera, inertial, and pressure sensors; while objects are detected with a deep neural network in the ROV camera stream. Validation experiments are performed for pose estimation, detection, and mapping. The litter detection dataset and code are made publicly available.

翻译：本文提出一种基于BlueROV2遥控水下航行器（ROV）的低成本实验室平台，用于开发和验证水下建图技术。该平台将ROV与待建图物体置于水池中，并通过顶部相机进行全局观测。在我们的原型建图应用中，通过融合顶部相机、惯性及压力传感器的测量数据，采用扩展卡尔曼滤波实现ROV位姿估计；同时利用深度神经网络对ROV相机视频流进行目标检测。研究针对位姿估计、目标检测与建图任务开展了验证实验。相关垃圾检测数据集与代码已开源发布。