Abstract
Determining the categories of different parts of a scene and generating a continuous traversable region map in the physical coordinate system are crucial for autonomous vehicle navigation. This paper presents our efforts in these two aspects for an autonomous vehicle operating in open terrain environment. Driven by the ideas that have been proposed in our Cognitive Architecture, we have designed novel strategies for the top-down facilitation process to explicitly interpret spatial relationship between objects in the scene, and have incorporated a visual attention mechanism into the image-based scene parsing module. The scene parsing module is able to process images fast enough for real-time vehicle navigation applications. To alleviate the challenges in using sparse 3D occupancy grids for path planning, we are proposing an approach to interpolate the category of occupancy grids not hit by 3D LIDAR, with reference to the aligned image-based scene parsing result, so that a continuous \(2\frac{1}{2}D\) traversable region map can be formed.
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Xiao, X., Ng, G.W., Tan, Y.S., Ye Chuan, Y. (2015). Scene Parsing and Fusion-Based Continuous Traversable Region Formation. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_28
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DOI: https://doi.org/10.1007/978-3-319-16628-5_28
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