UWB and IMU fusion positioning methods have been widely concerned for its high accuracy and robustness in GNSS-denied environment. However, most of the existing methods are stay under the Markov assumption and ignore the credible estimation of the yaw angle. In this paper, we propose a novel tightly-coupled IMU and multiple UWB tags fusion framework based on a graph optimization model that is able to provide high precision positioning and attitude determination service, which provides a new technical approach for the practical application of UWB and IMU fusion. A relative rotation and motion state initialization algorithm is designed and presented, which solves the problems that IMU installation direction is difficult to measure and the initial state is not stationary in practical application. Simulation experiments shows the impact of UWB tags distribution distance and IMU noise parameters on system performance, which provides design reference for practical applications. Finally, the real-site experiment proves the positioning RMSE of 4 cm based on our fusion solution and these simulation evidences, which confirms the great performance of our fusion solution.

The relative pose estimation is one of the most fundamental components for multi-robot systems, while it still remains an open and challenging research topic in infrastructure-free environment. In this letter, we target improving the accuracy and robustness of relative pose estimation for ground robotic teams, and propose to fuse range and odometry measurements to estimate the relative pose using sliding window optimization. In the system, multiple UWB tags for ranging are equipped on each robot, and visual inertial odometry is applied for estimating the ego-motion pose for each robot. Aiming for simple and effective relative pose initialization, the triangulation uncertainty for multi-tag robots is analyzed, and an initialization method is designed. To cope with the complex environments such as continuous NLOS condition, a NLOS detection and range measurements filtering method is presented. We have conducted series of experiments to demonstrate the performance of the proposed approach.

A novel realisable dual composite right/left-handed (D-CRLH) coplanar waveguide (CPW) transmission line (TL) in GaAs MMIC technology demonstrated by theoretical analysis and experimental results is presented. A D-CRLH TL is the dual of the conventional CRLH TL in the sense that it consists of series LC parallel tanks and shunt LC series tanks. However, an ideal D-CRLH TL cannot be realised because of unavoidable parasitic effects. The measured results indicated that a real D-CRLH TL exhibits a fundamentally different frequency response at higher frequency band in that a real D-CRLH TL cannot provide unlimited left handed (LH) bandwidth. Instead, it has triple bands; right-handed (RH) passband at lower frequencies (DC-2.9 GHz), LH passband at intermediate frequencies (5.1–14.3 GHz) and RH passband at higher frequencies (above 14.3 GHz). The reported fully integrated D-CRLH CPW TL metamaterial has a very compact size of 2.2 mm² which leads to a small loss 1.5 dB within 3 dB LH passband.

Fast and flexible dense reconstruction has been extensively studied due to its wide application. Considering the efficiency of multi-robot systems, cooperative reconstruction is gaining attention. Classic methods rely on inter-robot loop closures, which cannot work when there is no common area between robots. Ultra-wideband sensors provide distance measurement and can replace loop closures in multi-robot systems. However, ranging-based schemes do not solve the inconsistency of reconstruction, which is caused by odometry drift. We propose a range-aided cooperative localization and consistent reconstruction system. First, the system's front end tightly couples visual odometry and ranging to perform loop-independent cooperative reconstruction and reduce odometry drift. Second, the back end detects overlapping regions in submaps and proposes a novel dense pose graph optimization (PGO) step to further eliminate all trajectory drift and achieve consistent reconstruction. Extensive field experiments demonstrate the effectiveness and high performance of the system.

With the development of new distributed cluster applications such as distributed radar and UGV cooperation, the concept of relative positioning springs up—different from the classic positioning application, relative positioning focuses on the real-time determination of relative position relationships in multi-node networks without pre-deployment anchor, prior map and GNSS. Under such application conditions, relative localization also requires high real-time performance, and high accuracy in short baseline scenes, and can solve the positioning failure caused by occlusion and other factors. A high-precision relative positioning scheme based on UWB-INS fusion is proposed in this paper: a robust relative positioning algorithm based on iterative extended Kalman filter is optimized by modeling the system error state, and the adaptive residual elimination algorithm is introduced to ensure the robustness of positioning service. A series of experiments show that the scheme proposed in this paper can provide centimeter-level real-time positioning service for multi-node dynamic networks without anchors and maps, and is expected to become a potential effective relative positioning technology scheme.

PNT (positioning, navigation, and timing) is the core functional part of kinds of wireless sensor network, which can provide high‐precision timing and positioning services for cooperative work systems. Unfortunately, the mature wireless PNT schemes are generally based on GNSS and other auxiliary sources to complete the high accuracy synchronization process, which cannot be applied to GNSS degraded and denied environments such as mines, underground application. In order to solve the application problem of high‐precision wireless PNT, Hybsync—a novel non‐GNSS‐aided wireless PNT architecture, is proposed in this paper, which integrates the information from the UWB communication, inertial sensor, and camera to achieve great PNT performance. Hybsync improves the accuracy of time deviation measurement by collecting and recording timestamps in hardware layer, and with the coarse/fine synchronization two‐phase calibration, Hybsync greatly improves the accuracy of time deviation adjustment, thus providing accurate time information for the whole system. Besides, Hybsync uses the VINS framework to further integrate the real‐time information of IMU and camera to complete the multinode positioning service. Under the premise that the cost is much lower than existing solutions, Hybsync can provide nanosecond‐level clock synchronization and centimeter‐level positioning. Experiments prove that Hybsync supports high‐precision clock synchronization and positioning of more than 10 nodes; the maximum clock synchronization error is 3 ns, and the positioning error is 7 cm. It can provide accurate time and position services for cooperative work systems under complex and GNSS‐denied conditions.