1,771 research outputs found
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
Methodology and Algorithms for Pedestrian Network Construction
With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically.
A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks
Correction, update, and enhancement of vectorial forestry road maps using ALS data, a pathfinder, and seven metrics
Accurate information about forestry roads is a key aspect of forest management in terms of economy (e.g. accessibility, cost, optimal path) and ecology (e.g. wildfire and wildlife protection). In Canada, and in fact, globally, most provincial, state or territory governments maintain vectorial information on the forestry roads under their jurisdiction. However, official maps are not always accurate, may lack road attributes of interest and are not always up-to-date. Airborne Laser Scanning (ALS) has become an established technology to accurately characterize and map broad territories by providing high density 3D point-clouds with, at least, 3 or 4 measurements per square meter.
This paper addresses the problem of the automatic updating, fixing, and enhancement of vectorial forestry road maps over large landscapes (¿10000 km2). For this purpose, we developed a production ready, documented and open-source software. From metrics derived from the point-cloud the method produces a raster of road probability. It then uses an existing, inaccurate, map of the road network to define approximate start and end points for each road. Then, a pathfinder retrieves the accurate road shape by computing the least cost path between the two points on the probability raster. Using the accurate road position given by the algorithm, road width and road state are then estimated based the on characteristics of the point-cloud. We demonstrate that our algorithm retrieves the centrelines of roads in a natively vectorial form with an error below 3 m in 95% of the roads using a fully automatic method. The accuracy of the road location allows us to derive other accurate measurements, including the state of the roads
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