Point cloud registration from laser scanning data is a technique to establish the mapping relationship between source and target point clouds, which has been widely used in automatic 3D reconstruction, pose estimation, localization, and navigation. While algorithms like Super4PCS and MSSF-4PCS can achieve registration without initial poses, they are relatively slow, less accurate, and require iterations. To address these issues, we propose a 3D point cloud registration algorithm based on interval segmentation and multi-dimensional feature. Firstly, the source and target point clouds are segmented internally and the point cloud curvature is designed to narrow down the search range for the registration between the segmented point clouds. Secondly, the corresponding four-point sets in the segmented areas of the source and target point clouds are determined using affine invariance constraints. Finally, a multi-dimensional feature vector based on curvature features and fast point feature histogram is established to determine the unique corresponding four-point set pairs, and the rigid body transformation matrix is solved accordingly. Our algorithm is tested on publicly available 3D point cloud data models Bunny, Dino, Dragon, and Horse from Stanford University. Results showed that our algorithm improved registration accuracy by 24.39% and registration efficiency by 46.21% compared to the MSSF-4PCS point cloud registration algorithm. Multiple sets of experimental results confirmed the effectiveness of our algorithm. The proposed 3D point cloud registration is proved to be fast with high accuracy, which can be utilized for automatic segmentation, reconstruction, and modelling from Laser Scanning Data.

In the field of autonomous land vehicles (ALVs), environmental perception serves as the gateway for ALVs to acquire external information, directly determining the capabilities and intelligence level of autonomous driving. In the research of environmental perception technology for ALVs in field environments, achieving real-time, accurate, and robust obstacle detection is fundamental to realizing autonomous navigation. However, existing research often ignores the detection of negative obstacles, making the detection of positive and negative obstacles crucial for the safe navigation of ALVs. Therefore, we propose a method for ALV to use dual-LiDAR for positive and negative obstacles detection. The ground segmentation suffers from issues such as scene-specificity and threshold dependence. This method employs a horizontally mounted LiDAR to achieve ground segmentation and positive obstacle detection utilizing an adaptive threshold ground point cloud segmentation method with an improved ray method. Subsequently, a vertically mounted LiDAR is used to detect negative obstacles through local analysis of point clouds and clustering of feature point pairs to achieve local obstacle avoidance. A series of experiments were conducted in multiple field environments, and the results demonstrate that the proposed method achieves a successful detection rate of 98.5% for positive obstacles and 98.7% for negative obstacles in different field environments.

Semantic segmentation of aerial images is crucial in various practical applications, encompassing traffic management, search tasks, urban planning, and more. However, due to the unique shooting angles of aerial images, there are significant challenges in accurately segmenting objects, including large variations in object scales, deformations, and unclear features of small targets. To address this, we propose a multi-level feature mining network based on an encoder-decoder architecture called MLFMNet, aimed at excavating and integrating multi-level feature information in aerial images to enhance segmentation accuracy and robustness. MLFMNet leverages skip connections to obtain hierarchical feature representations from the encoder. Subsequently, through learnable fusion module (LFM) and feature reconstruction module (FRM) in the proposed decoder, it progressively fuses and reconstructs these features, thereby achieving accurate semantic segmentation. To tackle issues of significant size variations and deformations in objects, we design an irregular pyramid receptive field (IPRF) module embedded at the bottom of the encoder to capture receptive fields from multiple feature vectors, thus further mining abstract features. Moreover, to address the challenge of low segmentation and detection accuracy for small targets, a fine-grained feature mining (FGFM) module is embedded in the bottom of the decoder to capture spatial detail features. Particularly, MLFMNet-B achieves an mIoU of 70.8%, ranking fourth in the official leaderboard of the UAVid test set. The code is available at https://github.com/wxy16/MLFMNet .

Water body extraction from remote sensing images in complex backgrounds is crucial for environmental monitoring, disaster management, and urban planning. Although existing water body extraction algorithms for remote sensing images offer robust tools for monitoring, they still face challenges, including the inability to extract fine water bodies and the frequent omission of water body edges in complex backgrounds such as dense vegetation, varying terrain, or cloud interference. In response, we introduce an architecture named WatNet to enhance the precision of water body extraction in complex environments. WatNet comprises three main modules: the global multi-attention fusion module (GMAF), the water forward network module (WFN), and the edge focus attention module (EFA). The GMAF module enhances the model’s capability to capture global information through multi-head self-attention and convolutional attention modules, improving the overall feature extraction of water bodies. The WFN module utilizes depth-wise separable convolution and attention mechanisms to enhance the capture of local features in fine water bodies. The EFA module significantly improves the clarity and accuracy of water body boundaries through refined edge detection. Experiments on the LoveDA Remote Sensing Land Cover (LoveDA), Qinghai–Tibet Plateau (QTPL), and Wuhan dense labeling (WHDLD) datasets show that WatNet outperforms the mainstream methods in precision, recall, overall accuracy (OA), F1 score, and mean Intersection over Union (mIoU). On the LoveDA dataset, WatNet’s mIoU improved by 1.24% compared with the second-best method, by 0.4% on the QTPL dataset, and by 0.51% on the WHDLD dataset. These results validate the WatNet’s effectiveness and robustness in water body extraction tasks under various environmental conditions.

Accurate mathematical modeling of state of charge (SOC) prediction is essential for battery management systems (BMSs) to improve battery utilization efficiency and ensure a good safety performance. The current SOC prediction framework only considers battery-related features but ignores vehicle information. Additionally, in light of the emergence of time-series Transformers (TSTs) that harness the power of multi-head attention, developing a SOC prediction model remains a significant challenge. Therefore, we introduce a new framework that integrates laboratory battery data with mathematical vehicle model features to improve the accuracy of the SOC and propose a prediction model named DLPformer, which can effectively capture variations in the SOC attributed to both trend and seasonal patterns. First, we apply Matlab/Simulink to simulate a mathematical model of electric vehicles and process the generated vehicle data with Spearman correlation analysis to identify the most relevant features, such as the mechanical losses of the electric motor, differential, and aerodynamic drag. Then, we employ a data fusion method to synchronize the heterogeneous datasets with different frequencies to capture the sudden changes in electric vehicles. Subsequently, the fused features are input into our prediction model, DLPformer, which incorporates a linear model for trend prediction and patch-input attention for seasonal component prediction. Finally, in order to effectively evaluate the extrapolation and adaptability of our model, we utilize different driving cycles and heterogeneous battery datasets for training and testing. The experimental results show that our prediction model significantly improves the accuracy and robustness of SOC prediction under the proposed framework, achieving MAE values of 0.18% and 0.10% across distinct driving cycles and battery types.