Abstract
Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.
Highlights
The results show that in a global navigation satellite systems (GNSSs)-degraded environment, such as an urban canyon, if no measures are taken to control the GNSS data quality and the GNSS data have poor geometry, the performance of a dual-frequency multi-GNSS real-time kinematics (RTK)/MEMS IMU system may not be much better than that of a single-frequency system, and the dual-frequency receiver greatly increases the system cost
To evaluate the improved adaptive robust extended Kalman filter (AREKF) algorithm based on the single-frequency tightly coupled (TC) integrated multi-GNSS-TC RTK/UWB/inertial navigation system (INS)/map system proposed in this article, six schemes were designed and compared:
Scheme 6 uses the map to process NLOS, uses the improved AREKF algorithm to adjust the noise matrix at the same time to adjust the weight of the observation value of GNSS and UWB, effectively preventing the abnormal filtering state, and uses multi-GNSS-TC RTK to effectively control the accuracy of floating-point solutions in the outdoor environment
Summary
The demand for indoor positioning of AVs has accelerated the development of several mainstream technologies. Li et al [6] proposed a combination of TC single-frequency multiconstellation Global Navigation Satellite System (multi-GNSS) RTK and microelectromechanical system (MEMS)–inertial measurement unit (IMU) integration to solve the positioning problem in urban environments. Liu et al [26] proposed an innovation-based adaptive estimation adaptive KF (IAE-AKF) with an attenuation factor for integrated INS/GPS navigation of AVs. Chen et al [19] conducted research on the seamless positioning of indoor mobile robots and proposed an algorithm using the EKF and the least squares support vector machine (LS-SVM). UWB and map technology can be used for the localization of AVs in challenging indoor and transitional environments to improve the accuracy and continuity of positioning. This paper proposes an improved AREKF algorithm based on a single-frequency TC-integrated multi-GNSS-TC RTK/UWB/INS/map system to be applied to AVs. The map and the INS are used concurrently to identify and reduce UWB NLOS errors. The positioning experiment of using an experimental car to simulate AVs is carried out in a harsh and seamless environment, and the results of the experiment provide the possibility for the high precision and continuity of the positioning module in automatic driving
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