ECEF Position Accuracy and Reliability: Inertial Navigation with GNSS Precise Point Positioning (PPP)

This report presents experimental results for a moving platform using GPS PPP data for state estimation. Results from two PPP GPS state estimation approaches are presented: point-wise least squares (LS) and aided inertial navigation (INS). The point-wise LS results provide information about the accuracy and reliability of PPP GPS information at each measurement epoch, independent of other epochs. The INS results show the performance that can be achieved by combining information across measurement epochs. INS results are included for two different grades of IMU: navigation grade and consumer grade.The report cites publications that contain more detailed expla- nations of the GNSS error sources, computation of PPP wide area correction, and the LS and aided INS estimation algorithms

Challenges in Partially-Automated Roadway Feature Mapping Using Mobile Laser Scanning and Vehicle Trajectory Data

Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.Comment: 6 pages, 5 figure

Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

Global Navigation Satellite Systems (GNSS) provide positioning services for connected and autonomous vehicles. Differential GNSS (DGNSS) has been demonstrated to provide reliable, high quality range correction information enabling real-time navigation with sub-meter or centimeter accuracy. However, DGNSS requires a local reference station near each user, which for a continental or global scale implementation would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning (PPP) affords more flexibility as a public service for GNSS receivers, but its State Space Representation (SSR) format is not currently supported by most receivers. This article proposes a novel Virtual Network DGNSS (VN-DGNSS) design that capitalizes on the PPP infrastructure to provide global coverage for real-time navigation without building physical reference stations. Correction information is computed using data from public GNSS SSR data services and transmitted to users by Radio Technical Commission for Maritime Services (RTCM) Observation Space Representation (OSR) messages which are accepted by most receivers. The real-time stationary and moving platform testing performance, using u-blox M8P and ZED-F9P receivers, surpasses the Society of Automotive Engineering (SAE) specification (68% of horizontal error $\leqslant$ 1.5 m and vertical error $\leqslant$ 3 m) and shows significantly better horizontal performance than GNSS Open Service (OS). The moving tests also show better horizontal performance than the ZEDF9P receiver with Satellite Based Augmentation Systems (SBAS) enabled and achieve the lane-level accuracy which requires 95% of horizontal errors less than 1 meter.Comment: 14 pages, 8 tables, 4 figures, Code and data are available at https://github.com/Azurehappen/Virtual-Network-DGNSS-Projec

Outlier Accommodation for GNSS Precise Point Positioning using Risk-Averse State Estimation

Reliable and precise absolute positioning is necessary in the realm of Connected Automated Vehicles (CAV). Global Navigation Satellite Systems (GNSS) provides the foundation for absolute positioning. Recently enhanced Precise Point Positioning (PPP) technology now offers corrections for GNSS on a global scale, with the potential to achieve accuracy suitable for real-time CAV applications. However, in obstructed sky conditions, GNSS signals are often affected by outliers; therefore, addressing outliers is crucial. In GNSS applications, there are many more measurements available than are required to meet the specification. Therefore, selecting measurements to avoid outliers is of interest. The recently developed Risk-Averse Performance-Specified (RAPS) state estimation optimally selects measurements to minimize outlier risk while meeting a positive semi-definite constraint on performance; at present, the existing solution methods are not suitable for real-time computation and have not been demonstrated using challenging real-world data or in Real-time PPP (RT-PPP) applications. This article makes contributions in a few directions. First, it uses a diagonal performance specification, which reduces computational costs relative to the positive semi-definite constraint. Second, this article considers GNSS RT-PPP applications. Third, the experiments use real-world GNSS data collected in challenging environments. The RT-PPP experimental results show that among the compared methods: all achieve comparable performance in open-sky conditions, and all exceed the Society of Automotive Engineers (SAE) specification; however, in challenging environments, the diagonal RAPS approach shows improvement of 6-19% over traditional methods. Throughout, RAPS achieves the lowest estimation risk.Comment: 7 pages,2 figures, Accepted by 2024 American Control Conferenc

2D LIDAR Aided INS for vehicle positioning in urban environments

This paper presents a novel method to utilize\textit{2D} LIDAR for INS (Inertial Navigation System) aiding to improve\textit{3D} vehicle position estimation accuracy, especially when GNSS signals are shadowed.In the proposed framework, 2D LIDAR aiding is carried out without imposing any assumptions on the vehicle motion (e.g. we allow full six degree-of freedom motion).To achieve this, a closed-form formula is derived to predict the line measurement in the LIDAR's frame.This makes the feature association, residual formation and GUI display possible.With this formula, the Extended Kalman Filter (EKF) can be employed in a straightforward manner to fuse the LIDAR and IMU data to estimate the full state of the vehicle.Preliminary experimental results show the effectiveness of the LIDAR aiding in reducing the state estimation uncertainty along certain directions, when GNSS signals are shadowed