Almeida T., Santos V., Lourenço B., “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.
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.