Abstract
Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.
Highlights
Accurate and continuous navigation is one of the fundamental prerequisites for a reliable and intelligent driving system
We proposed a tightly coupled multi-Global Navigation Satellite Systems (GNSS) Precise Point Positioning (PPP)/Inertial Navigation System (INS)/ LiDAR method to improve the navigation performance in heavily urbanized areas
By formulating an Extended Kalman Filter (EKF) system driven by INS mechanization, the observations from LiDAR and PPP constructed the corresponding measurement updates to restrict the rapid error growth of INS
Summary
Accurate and continuous navigation is one of the fundamental prerequisites for a reliable and intelligent driving system. We propose a tightly coupled multi-GNSS PPP/INS/LiDAR (GIL) algorithm to perform three-Dimensional (3D) large-scale vehicle navigation in urban environments. Raw observations from multi-GNSS PPP, MEME-IMU, and 16-line LiDAR are integrated through an Extended Kalman Filter (EKF) to enhance the navigation performance in terms of position, velocity and attitude.
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