【摘要】 实现高精度的定位导航是深海采矿车完成海底工作任务的基础条件。在采矿车行进过程中，声呐设备生成的图像信息能够反映海底场景的变化，从而体现采矿车本身的运动，由此建立了一种声呐图像里程计，并将其与轮式里程计和USBL测量数据相结合提出一种深海采矿车组合定位导航算法。首先对多波束前视声呐图像进行预处理，然后使用Canny算法进行特征检测并对特征点云进行配准，再结合声呐成像原理构建声呐图像里程计运动模型，最后通过轮式里程计运动模型推导预测方程，声呐图像里程计运动模型和USBL测量数据推导更新方程，利用EKF(extended Kalman filter)算法实现基于多传感器融合的定位与姿态估计。海试数据验证了该组合定位算法能实现轮式里程计、声呐里程计和超短基线在速度、位置、艏向角估计、定位速率的精度互补，具有一定的有效性和精确性，该算法为深海采矿车的定位与导航算法研发提供了参考。更多还原

【Abstract】 The high-precision positioning and navigation is the basic condition for the deep-sea mining vehicle to complete the submarine work. In the process of the mining vehicle, the image information generated by the sonar can reflect the changes of the seabed scene, so as to reflect the movement of the mining vehicle itself. Therefore, a sonar image odometer is established, and a combined positioning and navigation algorithm of deep-sea mining vehicle is proposed by combining it with the wheel odometer and USBL measurement data. Firstly, multi-beam sonar images are preprocessed, then Canny algorithm is used for feature detection and feature point clouds are registered, and then the motion model of sonar image odometer is established based on sonar imaging principle. Finally, the prediction equation is derived from the motion model of the wheel odometer, the update equation is derived from the motion model of the sonar image odometer and USBL measurement data, and the localization and pose estimation based on multisensor fusion are realized by using EKF(extended Kalman filter) algorithm. Using data from the sea trial, it is verified that this integrated algorithm can realize the accuracy complementarity of the wheel odometer, the sonar image odometer and ultra-short baseline in speed, position, heading angle estimation and positioning rate, and has certain effectiveness and accuracy, which provides a reference for the research and development of the deep-sea mining vehicle localization and navigation algorithm.更多还原