ABSTRACT Dense point cloud registration is a critical problem in computer vision and 3D reconstruction, with widespread applications in scenarios such as robotic navigation, autonomous driving, and 3D measurement. However, dense point cloud registration faces significant challenges, including high computational complexity and prolonged processing times. To address these issues, this paper proposes a point cloud registration method based on multiple neighborhood feature difference (MNFD) that employs a coarse‐to‐fine strategy to effectively enhance both registration efficiency and accuracy. The proposed method consists of two stages: coarse registration and fine registration. In the coarse registration stage, a novel feature point extraction approach based on MNFD is introduced, capable of identifying highly stable and distinctive feature points in the point cloud. These feature points are then utilized in combination with the fast point feature histogram (FPFH) algorithm to achieve an initial alignment between the target and template point clouds. In the fine registration stage, the results from the coarse alignment are refined using algorithms such as iterative closest point (ICP) to ensure both efficiency and precision during the registration process. Experiments conducted on publicly available datasets demonstrate the superiority of the proposed method compared to existing approaches.

The extraction of regions of interest (ROI) plays a vital role in enhancing the precision of target analysis and identification, especially for hyperspectral small targets in wide fields of view. While the use of deep learning for ROI extraction has demonstrated significant potential, its efficacy has been hindered by a scarcity of labeled data. This study proposes a novel ROI extraction method employing a fully convolutional network and channel-spatial attention (CSFCN) for hyperspectral small targets through self-supervised learning. First, a strategy for pseudo-label assignment is designed based on the feature similarity and spatial continuity of hyperspectral images (HSIs). Second, an unsupervised segmentation model for HSIs is established, incorporating an attention mechanism based on FCN. Finally, ROIs are extracted based on segmentation results. The experimental results on two real-world HSIs, HSIa and HSIb, show that the proposed method can effectively improve the accuracy of extracted ROIs. The overall accuracy (OA) and the intersection over union (IOU) values of the proposed CSFCN reached 25.62%, 44.44% (HSIa), and 44.76%, 100% (HSIb), far exceeding the traditional unsupervised segmentation results. The superior experimental results demonstrate that the proposed method has promising application prospects.

Despite the intense effort from the research community, state-of-the-art WiFi-based human activity recognition (HAR) performance remains unsatisfactory. Current approaches usually use individual characteristics of CSI, i.e., amplitude or phase measurements, to model the relationship between the changes of channel state information (CSI) and human activities, which lead to their failure to achieve satisfactory accuracy due to information loss. To deal with this issue, this article proposes CeHAR, a CSI-based HAR using a channel-exchanging fusion network to deep fuse the CSI amplitude and phase features to obtain the informative features for HAR. The proposed CeHAR is a parameter-free dual-characteristic fusion framework that dynamically exchanges channels between subnetworks of two kinds of characteristics to comprehensively learn informative features from both. Specifically, the proposed approach employs two subnetworks using convolutional neural networks to learn features from each characteristic of CSI. The magnitude of the batch-normalization (BN) scaling factor is used to determine the channel importance of each characteristic, and then guides the exchange process. The proposed CeHAR also shares convolutional filters, but keeps private BNs layers in different characteristics, which, as an added benefit, allows our characteristic fusion network to be nearly as compact as a single-characteristic network. Extensive real-world experiments have been conducted to evaluate the performance of our proposed CeHAR, and the experimental results illustrate that our proposed approach outperforms baselines.

Abstract Depth information plays an important role in the vision‐related activities of robots and autonomous vehicles. An effective method to obtain 3D scene information is self‐supervised monocular depth estimation, which utilizes large and diverse monocular video datasets during the training process without the need for ground‐truth data. A novel multi‐task learning strategy that uses semantic information to guide the monocular depth estimation method while maintaining self‐supervision is proposed. An improved differential direct visual odometer (DDVO) combined with Pose‐Net is applied for achieving better pose prediction. Minimum reprojection loss with auto‐masking and semantic masking is used to remove the effects of low‐texture areas and moving dynamic‐class objects within scenes. Concurrently, the semantic masking is introduced into the DDVO pose predictor to filter moving objects and reduce the matching error between monocular sequence frames. In addition, PackNet is employed as the backbone of multi‐task learning to further improve the accuracy of deep prediction. The proposed method produces state‐of‐the‐art results for monocular depth estimation on the KITTI Eigen split benchmark, even outperforming supervised methods that have been trained using ground‐truth depth.

Recently, Scene Representation Networks (SRNs) have attracted increasing attention in computer vision, due to their continuous and light-weight scene representation ability. However, SRNs generally perform poorly on low-texture image regions. Addressing this problem, we propose superpixel-guided scene representation networks in this paper, called SG-SRNs, consisting of a backbone module (SRNs), a superpixel segmentation module, and a superpixel regularization module. In the proposed method, except for the novel view synthesis task, the task of representation-aware superpixel segmentation mask generation is realized by the proposed superpixel segmentation module. Then, the superpixel regularization module utilizes the superpixel segmentation mask to guide the backbone to be learned in a locally smooth way, and optimizes the scene representations of the local regions to indirectly alleviate the structure distortion of low-texture regions in a self-supervised manner. Extensive experimental results on both our constructed datasets and the public Synthetic-NeRF dataset demonstrated that the proposed SG-SRNs achieved a significantly better 3D structure representing performance.

Linear quadratic regulation (LQR) is very fundamental in modern control theory. The theory has been developing for several decades. However, a more general problem, the LQR problem for stochastic systems with multiple control channels and delays still remains outstanding. To better understand our idea, the paper focuses on dealing with the problem with two control channels and a delay, which is more difficult than the one with a single delayed control channel since the former actually encounters interaction of control channels besides the delay. A new value function is proposed, which is key for finding the predictor-like controller. The idea is also suitable for handling the LQR problem for Ito stochastic systems with multi-control-channel and multi-delay.

We investigate the optimal control problem for networked control systems consisting of a linear plant controlled by a remote controller and a local controller. By virtue of the maximum principle, we establish a non-homogeneous relationship between the state and the costate of this class of systems. Based on this relationship, the optimal controllers are derived in terms of the two Riccati equations.

This paper aims to give the efficient approach to the discrete-time systems with instantaneous and multiple time-delays measurements. Without resorting to the traditional augmentation and standard Kalman filtering formulation, a new approach termed as reorganized innovation analysis is developed in the paper. The improved performance is clearly demonstrated through analytical results and simulation experiments with multiple time-delayed measurements. More importantly, the approach presented in the paper forms the basis of solving the more complicated problems such as H/sub /spl infin// fixed-lag smoothing, multiple-step ahead prediction and H/sub /spl infin// control with control input multiple delays.