High Fidelity Localization and Map Building from an Instrumented Probe Vehicle.

다국어 초록 (Multilingual Abstract)

The lack of high fidelity data sources measuring roadway infrastructure has long handicapped the modeling of vehicular interaction and traffic flow. To date embedded loop detectors and other point detectors provide the data source for these mode.
Beginning in 2005, the Ohio State University began collecting high fidelity traffic flow data from an instrumented probe vehicle. The data mitigates a number of problems of both traditional data sources such as loops, and experimental data sourc.
This research resolves a number of issues with the instrumented probe vehicle data extraction by: 1) providing a method to validate a global localization estimates, 2) designing and implementing a new, observational, globally referenced mapping.
The novel use of a perception sensor, specifically a vertically scanning LiDAR, solves the issue of verifying a large, historic dataset&'s global positioning system derived global localization. This validation supports trust in instrumented prob.
To aid in the storage and retrieval of observational data of large, city-scale regions, this research creates the Map Oriented Grid, which supports the efficient global referencing of observational data stored in a grid structure. These grid str.