Autonomous Vehicles have become the focus of research around the world.Meanwhile, the continuous development of various emerging technologies in recent years has paved the way for the realization of fully autonomous driving.Furthermore, high-precision navigation and positioning systems are crucial to self-driving cars.For navigation and positioning technology of autonomous vehicles, Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation is of great significance to realizing high-precision navigation and positioning of autonomous vehicles.This paper researches and summarizes the development and current situation of GNSS, INS, and GNSS/INS integrated navigation technology.GNSS/INS integrated navigation algorithms are divided into four different models of combination methods: Ultra tightly Coupled, Tightly Coupled, Semi-tightly Coupled, and Loosely Coupled.Besides, the advantages and disadvantages of these four integrated navigation algorithms are compared and discussed.The characteristics of different GNSS/INS coupling models are summarized in the last chapter, and the potential research direction of GNSS/INS in the future is proposed. 

Research on GNSS INS & GNSS/INS Integrated Navigation Method for Autonomous Vehicles: A Survey

With the rise of location services applications, the need for navigation and positioning extends from isolated environments to a seamless, full domain. Aiming at the problem of low positioning accuracy in the transition area between indoor and outdoor, and it is difficult to achieve smooth switching, this article proposes an indoor and outdoor seamless positioning system for smartphones based on environment perception. Firstly, based on the hidden Markov model (HMM), we use smartphone observation data to select optimal features to construct an environmental perception model. Secondly, by increasing the magnetic field strength as an observation feature, the time delay for switching between different environments is shortened. Finally, based on the environmental perception results, a seamless indoor and outdoor positioning system is constructed. In the transition area between indoor and outdoor, to ensure the reliability of the positioning system, a Global Navigation Satellite System/ Inertial Navigation System/ Bluetooth Low Energy (GNSS/INS/BLE) integrated positioning model is proposed. In the outdoor and outdoor transition areas, we refine the stochastic model, which significantly improves the accuracy. To comprehensively evaluate the system performance, experiments are carried out in a complex environment. The experimental results show that the proposed perception model can accurately identify the changes of the environment, and the accuracy of judgment reaches more than 95%. It can support the seamless positioning system to select appropriate positioning models in different environments to achieve accurate and continuous positioning. The maximum positioning error does not exceed 0.5m. 

Environment Perception Based Seamless Indoor and Outdoor Positioning System of Smartphone

To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation.

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.

/pdf/a-method-of-setting-the-lidar-field-of-view-in-ndt-351fa6i4.pdf

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

Global navigation satellite systems (GNSS) such as the BeiDou Navigation Satellite System, Global Positioning System, Global Navigation Satellite System and Galileo are playing an increasingly pivotal role in autonomous driving. Given its importance for autonomous vehicles, positioning accuracy has become an important area of research. Stable and high-precision positioning is essential to ensure the safety of autonomous driving systems, and centimeter-level and lane-level positioning can, to a certain extent, achieve the required precision. Compared with a single GNSS, a multi-GNSS can provide greater positioning accuracy and availability. In the urban environment, buildings, viaducts, and tunnels can block GNSS signals. Thus, to obtain available and highly accurate positioning, an inertial navigation system (INS) can be helpful. Therefore, a tightly coupled multi-GNSS/INS system is introduced. To test the performance of this system, several typical urban scenes were selected. The results show that the system can provide centimeter-level positioning accuracy and maintain excellent positioning performance when GNSS signals are lost. This system may be used to meet the positioning requirements for autonomous driving until such time as urban road intelligence is comprehensively improved.

High-Precision Performance of Tightly Coupled Positioning in Urban Environment

As one of the commonly used solutions for vehicular dynamic positioning, the stability of Global Navigation Satellite Systems (GNSS)/ Inertial Navigation System (INS) integrated positioning when applied in an urban environment is still struggling. Generally, carrier-phase ambiguity fixing requires continuous several epochs, the stability and continuity of integrated positioning are significantly reduced if the signal is frequently blocked. To reduce the impact of frequent signal blockage, an improved tightly-coupled algorithm is proposed in this paper. Firstly, a step-wise ambiguity processing method is introduced to form instantaneous fixed Wide-Lane (WL) observations for calibrating INS measurement. Secondly, by estimating the differential inter-system bias (DISB) parameter, the pivot satellite can be shared between different constellations, to increase the number of usable satellites under the limited observation conditions and improve the positioning performance. The proposed method is verified with vehicular experiments on semi-simulated and actual urban canyon scenarios. In the artificial GNSS outage experiment, in the situation of 4 satellites that can be observed in BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS), the positioning accuracy of the proposed algorithm can achieve $4.1cm$ horizontally and $15.2cm$ vertically. An improvement of 26.9% horizontally and 20.4% vertically is gained accordingly compared to the conventional method. In the actual urban environment experiment, in case of insufficient satellite and low signal-to-noise ratio (SNR), when the conventional method can no longer maintain a fixed solution of GNSS/INS integrated positioning, the proposed algorithm can still achieve an accuracy of $49.7cm$ horizontally and $67.0cm$ vertically.

/pdf/an-improved-single-epoch-gnss-ins-positioning-method-for-343xv1nkr3.pdf

An Improved Single-Epoch GNSS/INS Positioning Method for Urban Canyon Environment Based on Real-Time DISB Estimation

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An Improved Single-Epoch GNSS/INS Positioning Method for Urban Canyon Environment Based on Real-Time DISB Estimation