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Impact craters are the most prominent topographic feature on the lunar surface, which will play a significant role in constructing lunar bases and lunar surface activities in the future. Traditional meteorite crater recognition methods are mainly based on artificial interpretation, usually combined with classical image processing methods. However, due to the different diameters and shapes of impact craters, the traditional crater identification methods have significant errors and low efficiency for small or overlapping impact craters. This paper proposes an automated algorithm termed nested attention-aware U-Net (NAU-Net) for crater detection using a lunar’s digital elevation model (DEM). It then uses template matching to effectively calculate the longitude, latitude, and radius of the crater. It is worth mentioning that NAU-Net is primarily based on UNet++ and attention networks and applies a sequence of nested dense convolution blocks, preferably of classical convolution, which combines U-Net++ and Attention Gates advantages. Since our network uses nested intensive attentional aware connections and in-depth supervision, the training process of the NAU-Net is simple, which can achieve more accurate detection and recognition. In fact, the network can recognize smaller or overlapping impact craters and larger and more complex ones. In honor of lunar impact craters, compared with U-Net, UNet++, Dense-Unet, Attention-Unet, R2-Unet, our model achieved recall rates and accuracy of 0.791 and 0.856, better than other improved U-Net models. The experimental results show that the NAU-Net model can be used to extract impact craters.

In the study of RGB-D SLAM (Simultaneous Localization and Mapping), two types of primary visual features, point and line features, have been widely utilized to calculate the camera pose. As an RGB-D camera can capture RGB and depth information simultaneously, most RGB-D SLAM methods only utilize the 2D information within the point and line features. To obtain a higher accuracy camera pose and utilize the 2D and 3D information within points and lines better, a novel geometric constraint model of points and lines (PL-GM) using an RGB-D camera is proposed in this paper. Our contributions are threefold. Firstly, the 3D points and lines generated by an RGB-D camera combining with 2D point and line features are utilized to establish the PL-GM, which is different from most models of point-line SLAM (PL-SLAM). Secondly, in addition to the 2D re-projection error of point and line features, the constraint errors of 3D points and lines are constructed and minimized likewise, and then a unified optimization model based on PL-GM is extended to the bundle adjustment model (BA). Finally, extensive experiments have been performed on two public benchmark RGB-D datasets and a real scenario sequence. These experimental results demonstrate that our method achieves a comparable or better performance than the state-of-the-art SLAM methods based on point and line features, and point features.

In this article, an adaptive leader-follower formation control on the basis of neural network (NN) is developed for a class of second-order nonlinear multi-agent systems with unknown dynamics. Unlike the first-order formation control that only needs to govern the position states, the second-order formation control needs to govern both the position and velocity variables. Hence the second-order formation is more challenging and interesting than the first-order case. In the control design, the adaptive NN approximator is employed to compensate the nonlinear uncertainties, so that the control design difficulty coming from the unknown dynamics is effectively overcome. Through Lyapunov stability analysis, it is demonstrated that the proposed control method can complete the control tasks. To further demonstrate the effectiveness, the formation method is implemented to a numerical simulation, and it shows the desired results.

Motion sickness is a common disease encountered in traditional vehicles as well as autonomous vehicles, which will negatively affect user acceptance. To make clear the pathogenesis of motion sickness, this study focused on drivers’ brain activity changes before and after motion sickness happens. Based on the six-degree-of-freedom driving simulator and noninvasive functional near-infrared spectroscopy (fNIRS), a database containing driving operation data synchronized with drivers’ brain activity record from 52 participants was collected under straight and curved driving conditions. The correlation analysis between motion sickness and changes of cerebral oxyhemoglobin concentration in the cerebral cortex was carried out based on this database. Results suggest that brain activity associated with motion sickness may differ under different driving conditions. However, the emergence of motion sickness responses is related to the occipital lobe under both driving conditions. Experimental results corroborate with several theoretical hypothesis about motion sickness in neuroscience. Consequently, this study proposes a new approach to research the mechanism of the correlation between motion sickness and cerebral cortex activity, which will contribute to developing the driving assistance system for preventing or alleviating motion sickness in autonomous vehicles.