Browsing by Subject "Mobile computing"
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Item Development of a Sensor Platform for Roadway Mapping: Part A - Road Centerline and Asset Management(Center for Transportation Studies, University of Minnesota, 2014-06) Davis, Brian; Donath, MaxCollecting information about the roadway infrastructure is a task that DOTs at all governmental levels need to accomplish. One way to increase the operational efficiency of these efforts is to use a relatively inexpensive mobile data collection platform that acquires information that is general enough to serve multiple purposes. The design and evaluation of one such platform that costs roughly $40,000 is described. It primarily consists of a differential GPS receiver providing vehicle location, and a LIDAR scanner that generates geometric profiles of the area between the vehicle and just beyond the road’s edge. The vehicle collects data along the road by driving it in both directions. The system post-processes the data to automate feature extraction. For roads with simple geometry such as two-lane, undivided highways, the road’s centerline can be calculated by finding the midline between the vehicle’s paths from each direction of travel. Algorithms process the LIDAR scans to automatically detect the presence of curbs and guardrails, which is then combined with location information to yield the position of these features in world coordinates. The centerline calculation was determined to be accurate to within 6 cm in areas where its use was applicable. Curbs and guardrails were generally detected with an accuracy of better than 10 cm. The results demonstrate that it is feasible to use a relatively inexpensive mobile data collection system to acquire road centerline and roadside features such as curbs and guardrails.Item Development of a Sensor Platform for Roadway Mapping: Part B – Mapping the Road Fog Lines(Minnesota Department of Transportation, 2015-04) Davis, Brian; Donath, MaxOur objective is the development and evaluation of a low-cost, vehicle-mounted sensor suite capable of generating map data with lane and road boundary information accurate to the 10 cm (4 in) level. Such a map could be used for a number of different applications including GNSS/GPS based lane departure avoidance systems, smart phone based dynamic curve speed warning systems, basemap improvements, among others. The sensor suite used consists of a high accuracy GNSS receiver, a side-facing video camera, and a computer. Including cabling and mounting hardware, the equipment costs were roughly $30,000. Here, the side-facing camera is used to record video of the ground adjacent to the passenger side of the vehicle. The video is processed using a computer vision algorithm that locates the fog line within the video frame. Using vehicle position data (provided by GNSS) and previously collected video calibration data, the fog line is located in real-world coordinates. The system was tested on two roads (primarily two-lane, undivided highway) for which high accuracy (<10 cm) maps were available. This offset between the reference data and the computed fog line position was generally better than 7.5 cm (3 in). The results of this work demonstrate that it is feasible to use a camera to detect the position of a road’s fog lines, or more broadly any other lane markings, which when integrated into a larger mobile data collection system, can provide accurate lane and road boundary information about road geometry.