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As Global Navigation Satellite Systems (GNSS) and inertial measurement units (IMUs) are gradually emerging as ubiquitous, utilizing redundant sensors to achieve accurate and robust large-scale navigation holds great promise. In this article, we propose a tightly coupled precise point positioning-real-time kinematic (PPP-RTK)/ IMU system with multiple homogeneous sensors to enable high-precision and high-availability vehicle navigation. In it, a stacked Kalman filter is employed to fuse raw pseudorange and carrier phase measurements from all GNSS terminals, driven by a core IMU designated randomly. In PPP-RTK processing, precise atmospheric and bias corrections from a GNSS server are leveraged to quickly resolve carrier phase ambiguity, thus ensuring centimeter-level positioning. Besides, rotation and translation constraints from redundant IMUs are imposed on the estimation pipeline to further improve the state estimation. Real-world experiments show that the proposed method can achieve an availability of 98.7% (horizontal position error $<$ 0.1 m) in a half-open-sky case, which is far better than the state-of-the-art methods. Also, redundant sensors not only contribute significantly to the integer ambiguity resolution, but are also able to promise continuous state estimation in the case of sensor failure.

Multi-agent cooperative positioning (CP) is widely applied in many applications of unmanned ground vehicles and unmanned aerial vehicles. Global Navigation Satellite System (GNSS) is an effective tool to conduct CP on a large scale. However, the performance of traditional GNSS-based CP algorithms is limited by the accuracy of single-agent self-positioning. In this paper, we propose a robust GNSS-based cooperative algorithm to achieve instantaneous precise positioning of multiple agents. In the proposed algorithm, precise point positioning-real-time kinematic (PPP-RTK) is utilized to conduct single-agent self-positioning, which is capable of achieving centimeter-level positioning rapidly with precise atmospheric corrections. Moreover, inter-agent associations derived from double-differenced carrier-phase observations and inter-epoch associations obtained via time-differenced carrier-phase observations are integrated with single-agent self-positioning by a factor graph-based optimizer. To evaluate the proposed scheme, real-world experiments with four agents in different scenarios were carried out. Based on the 30-minute field experimental results, the proposed scheme showed superior positioning performance with minimum and maximum three-dimension root mean square errors (3D-RMSEs) of 5.9 cm and 10.3 cm, respectively. It turned out that benefiting from PPP-RTK's characteristic of achieving precise positioning, the proposed method can converge to centimeter-level accuracy instantaneously.

In the realm of signal interference mitigation and precise attitude estimation, the utilization of multiple homogeneous global navigation satellite system (GNSS) antennas/terminals has demonstrated notable advantages. However, the potential benefits of homogeneous sensor fusion for enhancing position estimation have been relatively unexplored. In this article, we propose a tightly coupled (TC) precise point positioning (PPP)/inertial navigation system (INS) vehicle navigation algorithm without base stations, which can integrate an arbitrary number of homogeneous GNSS terminals with the aim of improving position estimation and speeding up system recovery from interference. Specifically, for the measurement representation, the original pseudorange and carrier-phase observations from all GNSS terminals are tightly incorporated with the inertial measurement unit (IMU) data to enable optimal state estimation. On the other hand, we exploit the known geometry between terminals and the spatially atmospheric correlation of observations to compress the states, thus to accelerate system convergence. Furthermore interestingly, since each GNSS terminal is peer-to-peer rather than master-slave in the proposed IMU-centric system, it enables a seamless operation without switching even in the event of an unexpected GNSS terminal failure. Real-world experiments have demonstrated that the proposed system can achieve a root mean square error (RMSE) of approximately 0.3 m for horizontal position estimation, and 92.24% availability at a 0.5-m boundary, outperforming the prevailing methods in terms of position estimation, state convergence, and performance in extreme scenarios.

Accurate ego-motion estimation is the foundation for autonomous vehicle navigation in complex urban environments. In multisensor fusion schemes, in addition to environment-related degradations, sensor measurements sometimes fail, which may have an unpredictable impact on navigation performance. To make the vehicle sensor system resistant to different types of failures, we propose a failure-resistant and lightweight multisensor fusion method, in which pseudorange and carrier phase from multiple global navigation satellite system (GNSS) constellations, sparse visual features from a monocular camera, and records from multiple low-cost micro-electromechanical system (MEMS) inertial measurement units (IMUs) are integrated at the measurement level by a sliding-window tightly coupled filter. Furthermore, considering the possibilities of the high-rate IMUs recovering from failures, we propose a flexible strategy to fully utilize all available IMU measurements without frequent reinitializations. To verify the proposed method, we perform extensive evaluations under different sensor failures (e.g., GNSS short-term blocking and long-term failures, image failures, and complex IMU failures and recoveries). The results show that our method outperforms conventional approaches that include only a single IMU and merely focus on sensor degradation, and resists various types of sensor failures. Despite initializing under a harsh environment and experiencing complex sensor failures, velocity and yaw estimations are significantly improved while a submeter 3-D positioning can be achieved in approximately 80% of the challenging scenario. For complex IMU failure and recovery records, our method can fully utilize all available IMU measurements and seamlessly switch between the base and auxiliary IMUs to recover trajectories that approximate the ground truth.

High-precision position, velocity, and orientation information is essential for vehicles to achieve autonomous driving. In this contribution, we propose a multi-sensor integration system for high-precision vehicle navigation, based on the fusion of GNSS PPP-RTK, MEMS IMU, and wheel odometer. Meanwhile, to fully use the physical characteristics of vehicle-specific motion, vehicle motion constraints (VMC) are used in conjunction with the wheel odometer. In the proposed system, the data from all sensors and vehicle motion constraints are tightly integrated into Kalman Filter. To validate the effectiveness of the proposed system, a series of real urban vehicle-borne experiments including different scenarios were conducted. Results indicate that using the proposed system, the position error RMS is (0.02 m, 0.02 m, 0.06 m) in the east, north, and vertical directions, with 95.2% availability of high-precision positioning (horizontal <10 cm, vertical <20 cm). The continuous and stable high-precision positioning could be maintained in the typical scenarios of GNSS degradation, such as boulevards, viaducts and tunnels. In addition, tests with simulated GNSS outages statistically demonstrate that this proposed system is capable of achieving 5.3 ‰ dead reckoning error with a maximum position error of 0.86 m in the case of simulated 30 s outages.

Continuous and reliable high-precision positioning in a wide area is an important task for autonomous driving. In this article, we present GIVE, a nonlinear optimization-based global navigation satellite system (GNSS)-inertial–visual (GIV) state estimator that tightly fuses multi-GNSS raw carrier phase and pseudorange observations with inertial and visual information to address the problem of continuous and reliable centimeter-level positioning in urban canyons where GNSS signal may be intermittent or even outage. The primary contribution of this work is the investigation of the high-precision carrier phase observations in terms of continuity and ambiguity resolution (AR) in GIV fusion. In particular, first, we propose a carrier phase-enhanced bundle adjustment (CPBA) method for accurate and drift-free GIV state estimation, in which the continuously tracked carrier phase arcs are used to establish the ambiguity constraints of current and historical states which have common-view satellites. The common-view ambiguity constraints are jointly optimized with IMU preintegration constraints and visual reprojection constraints under the factor graph framework. Furthermore, we develop a marginalization-based carrier phase ambiguity propagation (AP) method to leverage the continuity of the carrier phase and achieve an optimal estimation of the float ambiguity, which is essential for the correct AR of the carrier phase. An integer AR module, in combination with the proposed CPBA, enables the centimeter-level positioning accuracy of the GIV framework. We extensively evaluate the proposed method in both public datasets and challenging datasets collected in deep urban environments. Statistics show that the GIVE can provide smooth centimeter-level positioning results in open-sky conditions. Two challenging urban driving tests demonstrate that the GIVE can achieve continuous centimeter- to decimeter-level positioning accuracy and obtain 86.5%–94.1% 2-D positioning availability ( ${&lt; }10$ cm) and more than 94% 3-D positioning availability ( ${&lt; }30$ cm) in deep urban environments.