Abstract
This work introduces a ultra-fast object detection method named FLA-CNN for detecting objects in a scene from a planar LIDAR signal, using convolutional Neural Networks (CNN). Compared with recent methods using CNN on 2D/3D lidar scene representation, detection is done using the raw 1D lidar distance signal instead of its projection on a 2D space, but is still using convolutional neural networks. Algorithm has been successfully tested for RoboCup scene analysis in Middle Size League, detecting goal posts, field boundary corners and other robots. Compared with state of the art techniques based on CNN such as using Yolo-V3 for analysing Lidar maps, FLA-CNN is 2000 times more efficient with a higher Average Precision (AP), leading to a computation time of \(0.025\,ms\), allowing it to be implemented in a standard CPU or Digital Signal Processor (DSP) in ultra low-power embedded systems.
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Moussa, H., Gies, V., Soriano, T. (2023). Ultra-Fast Lidar Scene Analysis Using Convolutional Neural Network. In: Eguchi, A., Lau, N., Paetzel-Prüsmann, M., Wanichanon, T. (eds) RoboCup 2022: Robot World Cup XXV. RoboCup 2022. Lecture Notes in Computer Science(), vol 13561. Springer, Cham. https://doi.org/10.1007/978-3-031-28469-4_5
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