The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system.

The positioning service aided by low Earth orbit (LEO) mega-constellations has become a hot topic in recent years. To achieve precise positioning, accuracy of the LEO clocks is important for single-receiver users. To bridge the gap between the applicable time of the clock products and the time of positioning, the precise LEO clocks need to be predicted over a certain period depending on the sampling interval of the clock products. This study discusses the prediction errors for periods from 10 s to 1 h for two typical LEO clock types, i.e. the ultra-stable oscillator (USO) and the oven-controlled crystal oscillator (OCXO). The prediction is based on GNSS-determined precise clock estimates, where the clock stability is related to the GNSS estimation errors, the behaviors of the oscillators themselves, the systematic effects related to the environment and the relativistic effects, and the stability of the time reference. Based on real data analysis, LEO clocks of the two different types are simulated under different conditions, and a prediction model considering the systematic effects is proposed. Compared to a simple polynomial fitting model usually applied, the proposed model can significantly reduce the prediction errors, i.e. by about 40%-70% in simulations and about 5%-30% for real data containing different miss-modeled effects. For both clock types, short-term prediction of 1 min would result in a root mean square error (RMSE) of a few centimeters when using a very stable time reference. The RMSE amounts to about 0.1 m, when a typical real-time time reference of the national center for space studies (CNES) real-time clocks was used. For long-term prediction of 1 h, the RMSE could range from below 1 m to a few meters for the USOs, depending on the complexity of the miss-modeled effects. For OCXOs, the 1 h prediction could lead to larger errors with an RMSE of about 10 m.

Reliable continuing positioning is a critical requirement for intelligent transportation systems (ITS). An integrated positioning system is presented, where the global navigation satellite systems (GNSS) real‐time kinematic (RTK) method was mainly used. When RTK is not available, positioning was maintained by using Doppler measurements or by low‐cost inertial measurement unit (IMU) coupled with vehicle odometer measurements. A new integrity monitoring (IM) method is presented that addresses each positioning mode of the proposed integrated system. Models for the protection levels (PLs) are presented to bound the position error (PE) along the direction of motion of the vehicle and for the cross‐track direction. Both direction components are needed, for instance for collision avoidance and for lane identification. The method was assessed through a kinematic test performed in a dense urban environment. Results showed that by integrating GNSS RTK, Doppler with IMU + odometer, positioning was available all the time. For RTK, positioning accuracy was less than a decimetre and the IM availability was 99%, where the PLs bounded the PEs and were less than an alert limit of 1 m. Positioning using Doppler and IMU + odometer measurements bridged RTK breaks but at the sub‐meter level accuracy when used for short periods.

Various types of biases in Global Navigation Satellite System (GNSS) data preclude integer ambiguity fixing and degrade solution accuracy when not being corrected during precise point positioning (PPP). In this contribution, these biases are first reviewed, including satellite and receiver hardware biases, differential code biases, differential phase biases, initial fractional phase biases, inter-system receiver time biases, and system time scale offset. PPP models that take account of these biases are presented for two cases using ionosphere-free observations. The first case is when using primary signals that are used to generate precise orbits and clock corrections. The second case applies when using additional signals to the primary ones. In both cases, measurements from single and multiple constellations are addressed. It is suggested that the satellite-related code biases be handled as calibrated quantities that are obtained from multi-GNSS experiment products and the fractional phase cycle biases obtained from a network to allow for integer ambiguity fixing. Some receiver-related biases are removed using between-satellite single differencing, whereas other receiver biases such as inter-system biases are lumped with differential code and phase biases and need to be estimated. The testing results show that the treatment of biases significantly improves solution convergence in the float ambiguity PPP mode, and leads to ambiguity-fixed PPP within a few minutes with a small improvement in solution precision.