| Summary: | Road detection is a crucial concern in Autonomous Navigation and Driving Assistance. Despite the multiple existing algorithms to detect the road, the literature does not offer a single effective algorithm for all situations. A global more robust set-up would count on multiple distinct algorithms running in parallel, or even from multiple cameras. Then, all these algorithms’ outputs should be merged or combined to produce a more robust and informed detection of the road lane, so that it works in more situations than each algorithm by itself. This dissertation integrated in the ATLAS-CAR2 project, developed at the University of Aveiro, proposes a ROS-based architecture to manage and combine multiple sources of lane detection algorithms ranging from the algorithms that return the spatial localization of the road lane lines and those whose results are the navigable zone represented as a polygon. The architecture is fully scalable and has proved to be a valuable tool to test and parametrise individual algorithms. The combination of the algorithms’ results used in this work uses a confidence based merging of individual detections.
|
|---|