Abstract
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 and lane, so that it works in more situations than each algorithm by itself. This paper proposes a ROS-based architecture to manage and combine multiple sources of lane detection algorithms ranging from the classic lane detectors up to deep-learning-based detectors. The architecture is fully scalable and has proved to be a valuable tool to test and parametrize individual algorithms. The combination of the algorithms’ results used in this paper uses a confidence based merging of individual detections, but other alternative fusion or merging techniques can be used.
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This work was partially supported by project UID/CEC/00127/2019.
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Almeida, T., Santos, V., Lourenço, B. (2020). Scalable ROS-Based Architecture to Merge Multi-source Lane Detection Algorithms. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_20
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DOI: https://doi.org/10.1007/978-3-030-35990-4_20
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