777 research outputs found
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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 1.5 m and vertical error
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
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ECEF Position Accuracy and Reliability:Continent Scale Differential GNSS Approaches (Phase C Report)
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
The Grizzly, November 5, 2015
Wellness Increases Accessibility to Students • Trending: Discounted Jerseys • U-Imagine to Host Annual Bear Pitch Competition • A New Take on an Old Genre • STAT: More Than Just an Acronym • Opinions: Students Who Go the Distance; Sicario • Field Hockey Set to Defend Centennial Conference Title • Battle in Gettysburghttps://digitalcommons.ursinus.edu/grizzlynews/1676/thumbnail.jp
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