A private fifth generation (5G) network is a dedicated 5G network with enhanced communication characteristics, unified connectivity, optimized services, and customized security within a specific area. By subsuming the advantages of both public and non-public 5G networks, private 5G networks have found their applications across industry, business, utilities, and the public sector. As a promising accelerator for Industry 4.0, the concept of a private 5G network has recently attracted significant research attention from industry and academia. This article provides a comprehensive review of research on private 5G networks. Specifically, this paper first provides an overview of the concept and architecture of private 5G networks. It then discusses implementation issues and key enabling technologies for private 5G networks, followed by their more appealing use cases and existing real-life demonstrations. Finally, it examines some research challenges and future directions regarding private 5G networks.

Private 5G Networks: Concepts, Architectures, and Research Landscape

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization

Addressing the positioning problem of a mobile robot remains challenging to date despite many years of research. Indoor robot positioning strategies developed in the literature either rely on sophisticated computer vision techniques to handle visual inputs or require strong domain knowledge for nonvisual sensors. Although some systems have been deployed, the former may be lacking due to the intrinsic limitation of cameras (such as calibration, data association, system initialization, etc.) and the latter usually only works under certain environment layouts and additional equipment. To cope with those issues, we design a lightweight indoor robot positioning system which operates on cost-effective WiFi-based received signal strength (RSS) and could be readily pluggable into any existing WiFi network infrastructures. Moreover, a novel deep fuzzy forest is proposed to inherit the merits of decision trees and deep neural networks within an end-to-end trainable architecture. Real-world indoor localization experiments are conducted and results demonstrate the superiority of the proposed method over the existing approaches.

WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests

Accurate camera pose estimation is essential and challenging for real world dynamic 3D reconstruction and augmented reality applications. In this paper, we present a novel RGB-D SLAM approach for accurate camera pose tracking in dynamic environments. Previous methods detect dynamic components only across a short time-span of consecutive frames. Instead, we provide a more accurate dynamic 3D landmark detection method, followed by the use of long-term consistency via conditional random fields, which leverages long-term observations from multiple frames. Specifically, we first introduce an efficient initial camera pose estimation method based on distinguishing dynamic from static points using graph-cut RANSAC. These static/dynamic labels are used as priors for the unary potential in the conditional random fields, which further improves the accuracy of dynamic 3D landmark detection. Evaluation using the TUM \zjc{and Bonn RGB-D dynamic datasets shows that our approach significantly outperforms state-of-the-art methods, providing much more accurate camera trajectory estimation in a variety of highly dynamic environments. We also show that dynamic 3D reconstruction can benefit from the camera poses estimated by our RGB-D SLAM approach.

Accurate Dynamic SLAM using CRF-based Long-term Consistency.

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air–ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air–ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

Opportunistic Data Collection in Cognitive Wireless Sensor Networks: Air–Ground Collaborative Online Planning

Multiple mobile robots have gradually played a key role in many industrial systems, such as factory freight logistics system, patrol security in the factory environment, and multirobot collaborative service and work. As a key issue in industrial environment perception, the accurate robot localization can enhance their autonomous ability and is an important branch of robotic studies in artificial intelligence. In this paper, we propose a new method for cooperative autonomous localization among air-ground robots in a wide-ranging outdoor industrial environment. The aerial robot first maps an area of interest and achieves self-localization. Then the aerial robot transfers a simplified orthogonal perspective 2.5-D map to the ground robots for collaboration. Within the collaboration, the ground robot achieves pose estimation with respect to the unmanned aerial vehicle pose by instantaneously registering a single panorama with respect to the 2.5-D map. The 2.5-D map is used as the spatial association among air-ground robots. The ground robots estimate the orientation using automatically detected geometric information and generates the translation by aligning the 2.5-D map with a semantic segmentation of the panorama. Our method effectively overcomes the dramatic differences between the air-level view and the ground-level view. A set of experiments is performed in the outdoor industrial environment to demonstrate the applicability of our localization method. The proposed robotic collaborative localization outperforms most consumer sensor in accuracy and also has an outstanding running time.

Intelligent Collaborative Localization Among Air-Ground Robots for Industrial Environment Perception

Object-based simultaneous localization and mapping (SLAM) is a more natural and robust way for agents to interact with their surrounding environment. However, it introduces a problem of semantic objects association. Correct object association is the key factor to achieve a successful object SLAM system because object association and SLAM are inherently coupled and have not been well tackled yet. A novel formulation of the object association problem based on a hierarchical Dirichlet process (HDP) is proposed. Through the HDP, we can hierarchically associate the grouped object measurements. This can improve the object association accuracy and computation efficiency. Thanks to the novel formulation, the proposed method is also able to correct failure object associations according to its sampling inference algorithm. Furthermore, we introduce object poses to the processing of pose optimization. The object association and pose optimization are then solved in a tightly coupled way, by which both aspects can promote each other. The proposed method is evaluated on indoor and outdoor datasets and the experimental results show a very impressive improvement with respect to the traditional SLAM.

Hierarchical Topic Model Based Object Association for Semantic SLAM

An asynchronous on-line calibration method for multi-sensor fusion is disclosed. The mutual conversion relationship between the coordinate systems of the three sensors are calibrated, and the measurement information of the plurality of sensors is unified into the same coordinate system, The traditional calibration method is limited to the calibration between two sensors, and requires strict time synchronization, so the calibration process is more complex. The invention calculates the motion of the laser radar, the camera and the inertial measurement unit (IMU) respectively, obtains the motionof different sensors in the same time interval by using linear interpolation, and finally obtains the external rotation between two sensors by aligning the rotation sequence of the three sensors. Theinvention can obtain more accurate external orientation between the lidar, the camera and the inertial measurement unit (IMU), and provides initial value for subsequent calibration.

An asynchronous on-line calibration method for multi-sensor fusion

A semantic SLAM object association method based on a hierarchical topic model uses a depth learning model to detect objects in key frames and predict their positions and postures, When processing eachframe object, the Gibbs sampling method is used to sample the real environment object set with potential associated object according to the principle of view overlap, and the object association method is used to calculate for each object in the current frame, and the maximum posteriori probability is used to judge whether the object is associated or not. Factor maps are constructed for objects, cameras, and map points, and observations between them are used as edges to optimize object posture, camera posture, and map point positions. Finally, a complete semantic map including object information and camera trajectory is constructed. The invention can realize object association with high precision and avoids redundant object association. It can promote the camera pose estimation of semanticSLAM, and the optimized object pose can make the object association more accurate, so as to build a more accurate semantic map.

A semantic SLAM object association method based on a hierarchical topic model

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Intelligent Collaborative Localization Among Air-Ground Robots for Industrial Environment Perception

Hierarchical Topic Model Based Object Association for Semantic SLAM

An asynchronous on-line calibration method for multi-sensor fusion

A semantic SLAM object association method based on a hierarchical topic model