With the advancements of communication technologies and the Global Navigation Satellite Systems (GNSS), research and development in GNSS-based Cooperative Positioning (CP) is growing increasingly due to its potential of performance improvement compared to single-agent positioning. This paper presents a comprehensive survey that provides an overview of the state-of-the-art works on GNSS-based CP, categorizing them into six blocks: architecture, data sharing, measurements, estimation algorithms, performance evaluation, and integrity monitoring. Multiple solutions for each block are analyzed and compared if possible. Given that GNSS faces challenges mainly in urban areas, such as non-line-of-sight (NLOS), multipath, and cycle-slip, the discussions primarily focus on vehicular networks within urban environments. Finally, the challenges of practical applications and academic experiments of GNSS-based CP are discussed, and some potential research directions are outlined.

Initialization is a fundamental task for the state estimation of multi-sensor integration. In this paper, we propose a novel approach to achieve a fast and accurate initialization for the integrated system. With the outputs from GNSS, the rough roll, pitch, yaw, and direction of gravity are calculated. Based on these initial states, the GNSS/INS fusion and Visual-Inertial Odometry (VIO) are simultaneously launched whenever the observations are available. Subsequently, a coarse scale is retrieved by assigning the initial states of GNSS/INS fusion to the VIO procedure. The scale can be utilized to get poses and points very close to the real world. Once the VIO estimation converges, the transformation parameters between the poses of VIO and GNSS/INS fusion are estimated via non-linear optimization. Thus, we can align these two trajectories and evaluate the scale error. Through six vehicular tests, we analyze the performance of the proposed method with different illumination, obstructions, and motion patterns, and compare the results of the proposed approach with the state-of-the-art algorithms. The results indicated that the Visual-Inertial parameters converge within 9 seconds in our method. The scale errors of both our method are mostly within 10%. The alignment errors are mostly at centimeter-level in our method, which is comparable to the existing approaches.

In this paper, we aim to estimate the relative pose and focal length between two views with known intrinsic parameters except for an unknown focal length from two affine correspondences (ACs). Cameras are commonly used in combination with inertial measurement units (IMUs) in applications such as self-driving cars, smartphones, and autonomous aerial vehicles. The vertical direction of camera views can be obtained by IMU measurements. The relative pose between two cameras is reduced from 5DOF to 3DOF. We propose a new solver to estimate the 3DOF relative pose and focal length. First, we establish constraint equations from two affine correspondences when the vertical direction is known. Then, based on the properties of the equation system with nontrivial solutions, four equations can be derived. These four equations only involve two parameters: the focal length and the relative rotation angle. Finally, the polynomial eigenvalue method is utilized to solve the problem of focal length and relative rotation angle. The proposed solver is evaluated using synthetic and real-world datasets. The results show that our solver performs better than the existing state-of-the-art solvers.

Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a common direction. The degrees of freedom (DOFs) of the rotation matrix are reduced from 3 to 1. In this paper, we propose a globally optimal solver to calculate the relative poses and scale of generalized cameras with a known vertical direction. First, the cost function is established to minimize algebraic error in the least-squares sense. Then, the cost function is transformed into two polynomials with only two unknowns. Finally, the eigenvalue method is used to solve the relative rotation angle. The performance of the proposed method is verified on both simulated and KITTI datasets. Experiments show that our method is more accurate than the existing state-of-the-art solver in estimating the relative pose and scale. Compared to the best method among the comparison methods, the method proposed in this paper reduces the rotation matrix error, translation vector error, and scale error by 53%, 67%, and 90%, respectively.

Due to the ability to provide point clouds and Doppler velocity, as well as the adaptability in harsh weather conditions, 4D Radar has emerged as a new option for Simultaneous Localization and Mapping (SLAM). However, there is limited research on both robustness and computational efficiency, which are essential requirements for deploying 4D Radar as a specialized sensor in harsh weather. This paper proposes a fast and robust Radar-Inertial-Odometry (RIO) approach, named FAR-RIO. Specifically, we propose a dynamic point filtering based on full covariance propagation, along with an isotropic uncertainty model for accurate registration. In particular, leveraging the dual-observation capability of 4D radar, we design a novel dual-observation update pipeline for the iterated error-state Kalman filter (IESKF), coupled with a corresponding keyframe selection strategy, significantly reducing computational load while minimizing accuracy loss. Our method approaches state-of-the-art performance on various sensors and runs significantly faster than existing methods.