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Synchronizing Data Collection With GIS IDs To Map Time And Space

Our data collection process involves several different instruments. In order to tie these datasets together into one cohesive whole we rely on highly accurate timestamping all data. The data collection rig is outfitted with a mini computer by Intel called the NUC. It is a lightweight powerhouse that only sips power compared to machines of comparable specifications. The internal clock on the NUC is the master clock that all other instruments synchronize themselves to.

Mapping Everything to Space and Time: coordinate ID

In order for this data to be useful we must be able to accurately pinpoint where in time and space the data were collected. Knowing where the data were collected is important to be able to associate that data with that section of sidewalk and knowing when the data were collected is important to join all data together tightly. We use a single common key to handle this join.

The data are all joined on a common key called coordinate ID. This coordinate ID is an integer 19 digits long that consists of the unix epoch time down to milliseconds concatenated with the unique rig ID of six digits. The coordinate ID gives us a scalar temporospatial value that

is unique among all vehicles in the fleet. The coordinate ID 1556817834000000001 denotes the Unix Epoch time that would convert to Thursday, May 2, 2019 5:23:54 PM GMT and the rig id 001. We can impute the location between two gps coordinates to get a more accurate location for any coordinate ID.

Problem of Aligning Data with What it Actually Represents

The main problem to solve with the data is that there is an important distinction between where the observation was captured and the area the observation represents. The simplest situation to explain this is for the cameras. The camera mounted to the rear of the rig captures images of the sidewalk approximately 5 feet behind the rig. However if we were to simply associate an image with the coordinate ID of when it was captured it would be approximately 5 feet in front of what the image represents. Compounded on this is the fact that the GPS unit is not in the same place as the camera, it is a few inches in front (and a few feet below, but we are unconcerned with altitude for the camera data). To solve this problem we take measurements of the distances between the areas that the data is collecting data of and the GPS unit. Using these measurements we can offset the original coordinate ID of the image, lidar, or other observation by however many inches necessary. Additionally, since the adjusted coordinate IDs found using these offsets are very unlikely to have latitude and longitude points at that exact coordinate ID we will impute the latitude and longitude. For now we are using a simple linear imputation that assumes a constant speed between the two known points. Since these points are already between 0.2 and 0.1 seconds apart (depending on which GPS unit we are using) we think the linear model should be fine for imputing coordinates at any given coordinate ID.

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