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This paper presents a direct and non-singular approach based on an unscented Kalman filter (UKF) for the integration of strapdown inertial navigation systems (SINSs) with the aid of velocity. The state vector includes velocity and Euler angles, and the system model contains Euler angle kinematics equations. The measured velocity in the body frame is used as the filter measurement. The quaternion nonlinear equality constraint is eliminated, and the cross-noise problem is overcome. The filter model is simple and easy to apply without linearization. Data fusion is performed by an UKF, which directly estimates and outputs the navigation information. There is no need to process navigation computation and error correction separately because the navigation computation is completed synchronously during the filter time updating. In addition, the singularities are avoided with the help of the dual-Euler method. The performance of the proposed approach is verified by road test data from a land vehicle equipped with an odometer aided SINS, and a singularity turntable test is conducted using three-axis turntable test data. The results show that the proposed approach can achieve higher navigation accuracy than the commonly-used indirect approach, and the singularities can be efficiently removed as the result of dual-Euler method.

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In order to improve the detection speed of YOLOv5(You Only Look Once v5) in complex environments and dense target scenarios, a target detection method CN-YOLOv5(Cow Milk-You Only Look Once v5) improved YOLOv5 model is proposed. The traditional YOLOv5 network structure is improved, and the ability of the algorithm to extract features is improved by adding the SE (Squeeze and Excitation) attention module structure, and the accuracy of milk identification is improved. By improving the SPP (Spatial Pyramid Pooling) structure to SPPF (Spatial Pyramid Pooling Fast) structure, the detection speed is accelerated, and the CN-PAN (Cow Nipple Path Aggregation Network) model is proposed based on the PAN (Path Aggregation Network) module. Based on the PAN structure in the traditional YOLOv5 network, the iteration of small target detection is lightweight. Based on YOLOv5s, the milk image dataset CNmodel-YOLOV5s(Cow Milk model-You Only Look Once v5) was created. Experimental results show that the two algorithms can be tested before and after the improvement by using the milk dataset CNmodel-YOLOV5s. The improved algorithm on the test equipment increases the detection speed by up to 13% with almost no impact on accuracy. The improved YOLOV5 algorithm can identify milk targets more quickly, which provides theoretical support for subsequent detection of medium and large targets in complex environments and dense target scenarios.