The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, 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 a 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 sub-meter level horizontal positioning accuracy in GNSS-challenging environments, with improvements of (73.3%, 59.7%, 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.

Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars and autonomous robots. However, it is often difficult for a standalone sensor to fulfill the demanding requirements of precise navigation in complex scenarios. To fill this gap, this letter proposes to exploit the complementariness of the global navigation satellite system (GNSS), inertial measurement unit (IMU) and vision via a tightly coupled integration method, aiming to achieve continuous and accurate navigation in urban environments. Specifically, the raw GNSS carrier phase and pseudorange measurements, IMU data, and visual features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF) to make full use of the multi-sensor information and reject potential outlier measurements. Furthermore, the widely used high-precision GNSS models including precise point positioning (PPP) and real-time kinematic (RTK) are unified in the proposed integrated system to increase usability and flexibility. We validate the performance of the proposed method on several challenging datasets collected in urban canyons and compare against the loosely coupled and state-of-the-art methods.

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.

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.

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.

Continuous and reliable high-precision positioning in a wide area is an important task for autonomous driving. In this paper, we present GIVE, a nonlinear optimization-based 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 pre-integration 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 the GIVE can achieve continuous centimeter to decimeter-level positioning accuracy and obtain 86.5-94.1% 2D positioning availability (<10 cm) and more than 94% 3D positioning availability (<30 cm) in deep urban environments.