Recent research have devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere wireless signal propagation, that manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in certain speed and scale, recent works commonly have moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi based human activity recognition system that synthesize variant activities data through 8 CSI transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep learning model that cater to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

RGB-D-based human action recognition has attracted much attention recently because it can provide more complementary information than a single modality. However, it is difficult for two modalities to effectively learn spatial-temporal information from each other. To facilitate information interaction between different modalities, a cross-modality compensation convolutional neural network (ConvNet) is proposed for human action recognition, which enhances the discriminative ability by jointly learning compensation features from the RGB and depth modalities. Moreover, we design a cross-modality compensation block (CMCB) to extract compensation features from the RGB and depth modalities. Specifically, CMCB is incorporated into two typical network architectures, ResNet and VGG, to verify the ability to improve the performance of our model. The proposed architecture has been evaluated on three challenging datasets: NTU RGB+D 120, THU-READ and PKU-MMD. We experimentally verify that our proposed model with CMCB is effective for different input types, such as pairs of raw images and dynamic images constructed from the entire RGB-D sequence, and the experimental results show that the proposed framework achieves state-of-the-art performance on all three datasets.

6-D pose estimation is an important branch in the field of vision measurement and is widely used in the fields of robotics, autonomous driving, and reality augmentation. The latest research trend in 6-D pose estimation is to train a deep neural network to directly predict the 2-D projection position of the 3-D keypoint from the image, establish the corresponding relationship, and, finally, use the perspective-n-point (PnP) algorithm to perform pose estimation. The current challenge of pose estimation is that, when objects are textureless, occluded, or scene-cluttered, the detection accuracy is reduced, and most of the existing algorithm models are large and cannot accommodate real-time requirements. In this article, we introduce a densely connected feature pyramid network (DFPN) that can efficiently integrate and utilize features. We combine the cross-stage partial network (CSPNet) with DFPN to design a new network for 6-D pose estimation, DFPN-6-D, a new approach for 6-D object pose estimation. DFPN-6-D can efficiently and accurately handle objects with textureless, occluded, and scene clutter and estimate their full 6-D poses in a single shot. Furthermore, we propose a new confidence calculation method and loss function for object pose estimation, which can fully consider spatial information. Finally, we propose a novel augmentation method for direct 6-D pose estimation approaches to improve performance and generalization ability in the case of occlusion, which is called 6-D augmentation. Our approach achieves a new state-of-the-art accuracy of 98.06 and 87.09 in terms of the ADD(-S) metric on the Linemod dataset and the Occluded-Linemod dataset, and our method also achieves the best result in terms of the different metric on the MULT-I dataset, the BIN-P dataset, and the T-LESS dataset, respectively, while still running end-to-end at over 65 frames/s. The experimental results demonstrate that our algorithm is robust to textureless materials and occlusion while running more efficiently than other methods. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose.

Time-varying formation (TVF) and trajectory tracking $H_{\infty }$ control problem of multiagent systems (MASs) subject to communication delays and external disturbances under the directed communication topology is studied. This article’s objective is for all agents to attain the desired TVF and track the pregiven formation center trajectory simultaneously. First, a distributed TVF and trajectory tracking control protocol employing neighborhood interaction information is developed in the presence of communication delays. Second, since the Laplacian matrix of a graph can be decomposed into the product of two specific matrices, the TVF and trajectory tracking $H_{\infty }$ control problem is converted into the lower dimension asymptotic stability problem of a closed-loop system by applying an appropriate variable conversion. Third, a Lyapunov–Krasovskii functional is constructed to analyze the stability of MASs. Sufficient conditions are obtained in the form of linear matrix inequalities (LMIs) to ensure the completion of the TVF and formation center trajectory tracking of MASs. In the meantime, the maximum allowable communication delay can be calculated by the LMIs. Finally, the results of numerical simulations are presented to verify the validity of the approach this article proposes.

Six-dimensional (6D) pose estimation is an important branch in the field of robotics focused on enhancing the ability of robots to manipulate and grasp objects. The latest research trend in 6D pose estimation is to directly predict the positions of two-dimensional (2D) keypoints from a single red, green, and blue (RGB) image through convolutional neural networks (CNNs) and establish a corresponding relationship with the three-dimensional (3D) keypoints of the model. Then, the perspective-n-point (PnP) algorithm is used to recover the 6D pose parameters. Currently, two challenges are encountered in pose estimation based on an RGB image. On the one hand, an RGB image lacks depth information, and it is thus difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. In this paper, we propose a bidirectional depth residual fusion network with a depth prediction (DP) network to estimate the 6D poses of objects (BDR6D). The BDR6D network predicts the depth information of objects using an RGB image, converts the depth information into point cloud information, and performs feature extraction and representation together with the RGB information during the feature extraction and representation stages. Specifically, the RGB image is fed into the BDR6D network, the DP network predicts the depth information of the objects in the image, and the depth map and RGB image are input into a point cloud network (PCN) and CNN, respectively, for feature extraction and representation. We build the bidirectional depth residual (BDR) structure so that the CNN and PCN can share information during feature extraction and representation. This approach allows the two networks to use each other’s local and global information to improve feature extraction and representation. For the keypoint selection stage, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of the object of interest. We evaluate the proposed method with three benchmark datasets and compare it with other 6D pose estimation algorithms. The experimental results show that our method outperforms the state-of-the-art approach. Finally, we deploy our proposed method in conjunction with the Universal Robots 5 manipulator (UR5) robot to grasp and manipulate objects. Note to Practitioners —The purpose of this paper is to solve the problem of 6D pose estimation for robot grasping. The existing RGB image-based pose estimation approach faces two challenges. On the one hand, a single RGB image lacks depth information, so that it is difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. To solve the above problems, a novel network that can predict the depth information of objects from an RGB image and fuse the depth information with the RGB information to estimate the 6D pose of objects is proposed. Furthermore, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of objects of interest. We evaluate the proposed approach based on three benchmark datasets and the UR5 robot platform and verify that our method is effective.