Antenna miniaturization technologies have always been the focus of antenna research. In recent years, mechanical antennas whose size does not depend on the electromagnetic wave-length have attracted researchers’ attention. Acoustically excited antennas (AEAs), as a kind of mechanical antenna, can achieve reciprocal transmission and reception. AEAs utilize internal charge and magnetization oscillations of piezoelectric materials and magnetoelectric (ME) composite materials, respectively, to radiate electromagnetic waves, which enables the size of the antenna to be related to the wavelength of the acoustic wave, reducing the size of the antenna by 4-5 orders of magnitude over conventional antennas. Additionally, it is expected to achieve breakthrough application changes in the fields of mobile low frequency antennas, portable shortwave antennas, and chip-based microwave antennas. In this paper, the theory of AEAs is summarized and different modeling and measurement studies of AEAs are described in detail. Finally, the challenges of AEAs research are elaborated and future research on AEAs is prospected.

Visual-inertial odometry (VIO) often suffers from drifting, particularly in large-scale environments. Concurrently, the global navigation satellite system (GNSS) signals will be intermittent or even inaccessible in obstructed environments. In this work, we present a robust, real-time GNSS-visual-inertial state estimator, abbreviated as R2-GVIO, that achieves drift-free six-degree-of-freedom accurate global positioning in urban challenging environments. Specifically, we integrate GNSS into the optimization-based monocular and stereo VIO, enabling the fusion of GNSS real-time kinematic (RTK), reprojection error, and inertial measurement unit (IMU) preintegration within a factor graph framework. In addition, to enhance the R2-GVIO’s robustness in GNSS-unfriendly environments, we propose an online GNSS measurement outlier detection and culling algorithm based on velocity constraints and a time synchronization mechanism. Furthermore, we propose a GNSS-aided initialization method that provides accurate gravity direction, IMU zero bias, and local–global external parameters. Moreover, we adopt the marginalization strategy of covisibility graph keyframes (KFs) and add the GNSS factor constraint on the covisibility KFs pose, ensuring the real-time performance of the system. Experiments on public data sets and real-world experiments verify that in challenging environments, including urban canyons, complex indoor–outdoor, and large-scale environments, the positioning accuracy and robustness of the proposed R2-GVIO algorithm are superior to the state-of-the-art algorithms VINS-Fusion, ORB-SLAM3, IC-GVINS, and GICI-LIB. To make contribute to the community, we open sourced the complex environment data sets https://github.com/SYSU-CPNTLab/SYSU-Campus-GVI-Dataset (“SYSU-Campus-GVI” on GitHub).

In this work, we present an acoustic-visual-inertial navigation system (Acoustic-VINS) for underwater robot localization. Specifically, we address the problem of the global position of the underwater visual-inertial navigation system being inappreciable by tightly coupling the long baseline (LBL) system into an optimization-based visual-inertial SLAM. In our proposed Acoustic-VINS, the reprojection error, IMU preintegration error, and raw LBL measurement error are jointly minimized within a sliding window factor graph framework. Furthermore, we propose an acoustic-aided initialization method to exhibit an accurate initial state for successful state estimation. Additionally, for wider application, we extend the sensor data of the real-world AQUALOC dataset to obtain the LBL-AQUALOC dataset. Experimental results on the ten sequences of the LBL-AQUALOC dataset in challenging underwater scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM.

The application of strap-down inertial navigation system/long baseline system (SINS/LBL) has been proven to be an effective method to solve the accumulated position error of autonomous underwater vehicles (AUVs). However, height positioning in the SINS/LBL system is generally unstable. Acoustic differential technology uses single- or double-difference mode to weaken the influence of system errors on signal propagation time, but the vertical positioning result of this method is unstable. Moreover, the accumulative errors of inertial navigation will cause the height positioning errors to diverge over time. In view of the situation that the height positioning results are prone to divergence, the SINS/LBL tightly coupled (SLT) system is studied and an adaptive method based on the improved Sage–Husa adaptive filter is proposed. This method adaptively adjusts the system noise matrix and then utilizes depth information of the pressure sensor (PS) to adaptively estimate the vertical direction errors. To verify the feasibility of the proposed algorithm, the simulation experiments compare the positioning results of three fusion methods that are centralized Kalman filter (CKF), federated Kalman filter (FKF), and adaptive height-constraint Kalman filter (AHKF). Simulation results show that the AHKF algorithm improves the precision and stability of underwater SLT navigation system significantly. To some extent, the AHKF algorithm addresses the problem of accumulative errors in the vertical direction in the SLT system. Also, the improvement of height positioning accuracy is hugely helpful to AUV complete mission successfully.