Precise position information plays a crucial role in industries such as intelligent unmanned systems and autonomous driving. Combining the advantages of traditional precise point positioning (PPP) and real-time kinematic (RTK) technologies, the emerging PPP-RTK technique is able to provide real-time centimeter-level positioning in open environments. However, its stability is often insufficient in complex environments due to frequent interference of satellite signals. To enhance the continuity and robustness of positioning results in such challenging environments, inertial navigation system (INS), which can perceive the motion information of the carrier, is commonly integrated with global navigation satellite system (GNSS). In this paper, an optimization-based tightly-coupled multi-GNSS PPP-RTK/INS system is proposed to achieve continuous and reliable positioning in urban environments. In the proposed algorithm, an estimator based on Maximum A Posteriori (MAP) estimation is utilized to integrate multi-GNSS raw observations, inertial measurements and precise atmospheric information. Moreover, the outlier detection algorithm based on standardized residuals is applied in the proposed method to reject gross errors introduced by GNSS observations, making the positioning results more consistent with observations to get reliable ambiguity-float solutions. Then, a cascaded ambiguity resolution is conducted to obtain final precise ambiguity-fixed solutions. The experimental results show that the proposed method achieves centimeter-level horizontal position estimates in urban environments, with an accuracy improvement of over 80% compared to PPP-RTK. Besides, the proposed optimization-based algorithm represents an accuracy improvement of approximately 30% compared to the traditional extended Kalman filter-based method.

Continuous and reliable estimation of navigation states is of paramount importance in ensuring the safe operation of intelligent vehicles. The conventional global navigation satellite system (GNSS)-Inertial-Visual navigation systems have demonstrated the capability to achieve locally accurate and globally drift-free pose estimation. However, challenges such as frequent satellite signal interference, limited visual features, and sudden sensor failures can severely degrade performance or even lead to complete system collapse when utilizing minimal sensor configurations. For this reason, we propose a novel and globally drift-free tightly coupled (TC) system that can integrate any number of GNSS, inertial measurement units (IMUs), and cameras to enable accurate and robust vehicle navigation. Specifically, a stacked state estimator centered on the IMU is designed to fuse information from all sensors at the raw measurement level. The pseudorange and carrier phase measurements from all GNSS terminals are directly correlated with the core IMU, ensuring accurate and fast positioning of the system in a global frame. The feature measurements from multiple independent cameras are also used to update the states by exploiting inter-epoch geometric constraints. In addition, the multiple homogeneous IMUs can not only further improve the state estimation of the system by imposing rigid constraints on the core IMU, but also switch over in time for smooth state estimation when the core IMU are faulty. We comprehensively evaluate the state estimation accuracy and robustness of the proposed approach through a series of in-vehicle experiments and simulation experiments in real urban scenarios. The results indicate that the proposed system can achieve 93.2% availability with a horizontal position error less than 0.5 m and 97.8% availability with a heading error less than 0.2 deg in typical urban environments, significantly outperforming both conventional and state-of-the-art approaches. Note to Practitioners—This study focuses on the tight integration of multiple homogeneous and heterogeneous sensors with the goal of addressing frequent interference and degradation challenges in wide-area vehicle navigation applications. We propose a general-purpose GNSS-Inertial-Visual tight coupled framework capable of integrating any number of GNSS, IMUs, and cameras at the raw measurement level. It maximizes the use of as much sensor information as possible to achieve accurate and robust state estimation and is resilient to anomalous measurements and sensor unavailability. This solution holds practical and effective for autonomous vehicles that are now commonly equipped with multiple sensors.

Accurate position, velocity and orientation are essential for the autonomous navigation of unmanned vehicles. The integration of GNSS and INS that can deliver continuous navigation states is widely used for intelligent vehicle systems. In this paper, we propose a tightly coupled GNSS/INS positioning framework with carrier-phase ambiguity resolution based on factor graph optimization (FGO). In this approach, a sliding window optimizer is employed to fuse the multi-GNSS pseudorange and carrier-phase observations with inertial measurements. The same ambiguity within the window is considered as a state node, and the constraint on the ambiguity is continuously preserved by marginalization. To further improve the accuracy and reliability of precise positioning, the carrier-phase ambiguity resolution is introduced to the FGO-based GNSS/INS framework. When the vehicle is detected to be stationary, a zero-velocity constraint and an attitude invariant constraint will be imposed. Several experimental results indicate that the proposed method can accomplish the centimeter-level position estimation performance with beyond 90% positioning availability (horizontal$<$10 cm and vertical$<$10 cm) and outperforms the current state-of-the-art filter-based tightly coupled method.

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it encounters challenges in vehicular scenarios, as the restricted motion of a ground vehicle could lead to degraded observability, and a lack of stable features might occur in dynamic road environments. In this paper, we propose Ground-VIO, which utilizes ground features and the specific camera-ground geometry to enhance monocular VIO performance in realistic road environments. In the method, the camera-ground geometry is modeled with vehicle-centered parameters and integrated into an optimization-based VIO framework. These parameters could be calibrated online and simultaneously improve the odometry accuracy by providing stable scale-awareness. Besides, a specially designed visual front-end is developed to stably extract and track ground features via the inverse perspective mapping (IPM) technique. Both real-world experiments and tests on public datasets are conducted to verify the effectiveness of the proposed method. The results show that our implementation could dramatically improve monocular VIO accuracy in vehicular scenarios, achieving comparable performance to state-of-art stereo VIO solutions and showing good robustness in challenging conditions. The system can also be used for the auto-calibration of IPM which is widely used in vehicle perception. A toolkit for ground feature processing, together with the experimental datasets, has been made open-source.

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 5.3dead reckoning error with a maximum position error of 0.86 m in the case of simulated 30 s outages.

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, the global navigation satellite system (GNSS) is recognized as an important approach for worldwide positioning services. However, its application is limited in urban areas due to severe signal attenuation, reflections, and blockages. Inertial navigation system (INS) can provide high-precision navigation outputs within a short period, but its accuracy suffers from error accumulation, especially when equipped with the low-cost microelectromechanical system (MEMS) inertial measurement units (IMUs). In addition, light detection and ranging (LiDAR) is becoming more common as an option in vehicles, which can detect rich geometric information in the environment for ego-motion estimation. Aiming at taking advantage of the complementary characteristics of these onboard technologies to navigate in urban environments, a tightly coupled multi-GNSS precise point positioning (PPP)/INS/LiDAR integrated system is proposed. We also develop an LiDAR sliding-window plane-feature tracking method to further improve navigation accuracy and computational efficiency. The performance of the proposed integrated system was evaluated in vehicular experiments with different GNSS observation conditions. Results indicate that our proposed GNSS/INS/LiDAR integration can maintain submeter level horizontal positioning accuracy in GNSS-challenging environments, with improvements of (73.3%, 59.7%, and 64.2%) compared to traditional GNSS/INS integration. Moreover, the plane-feature tracking method is proved to outperform traditional point-to-line and point-to-plane scan matching in terms of accuracy and efficiency.

The integration of inertial navigation system (INS) and global navigation satellite system (GNSS) with a single antenna is widely applied in consumer-level vehicle navigation. However, it is challenging for a low-cost single-antenna GNSS/INS integrated system to provide reliable, consistent state estimation under low-dynamic scenarios (e.g., low-speed robotic applications) as the limited dynamics could lead to degradation of the system observability. We propose a novel moving-antenna GNSS/INS integration method, in which the antenna is designed to perform specific motions on the platform during vehicle maneuver, thus to improve the system observability in degraded conditions, bringing about the advantage of multi-antenna GNSS using a single antenna. It turns out that a moving antenna with a maximum speed of just 0.15 m/s would improve the system performance dramatically by applying raw GNSS carrier phase observations into the integration. In the proposed method, firstly, a moving-antenna fast initial alignment algorithm is developed, which enables the system to perform instantaneous initial alignment in static. Secondly, for GNSS/INS integration filter, the moving-antenna scheme improves the heading angle estimation accuracy by more than 50% under typical dynamic scenarios, achieving comparable performance to dual-antenna GNSS/INS integration. This work shows that it is possible and even beneficial to continuously change the inter-sensor transformation in an integrated navigation system.