Abstract
The application environment of mobile robot is gradually expanding from indoor to outdoor. Vision-based detection, which acquires traffic information through the camera, is a state-of-the-art auxiliary technology. In this paper, a robotic middleware Robot Operating System (ROS) is applied to detect object and control application based on embedded processor. And, we present an effective On-road object detector which is suitable for embedded GPU by improving the performance of Single Shot MultiBox Detector (SSD). Our approach is to construct detection network by using depth-wise separable convolution for saving computing resource and present multi-category clustering to adjust the generated default boxes for optimizing accuracy. Experiments on KITTI dataset show that the proposed network runs 2.1 times faster than original SSD network on embedded GPU and maintains 71% mean average precision. Finally, a mobile robot is designed based on the detector and controller to demonstrate On-road assisted driving intuitively.
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Wang, G., Song, Q., Li, T., Li, M. (2022). Fast On-Road Object Detector on ROS-Based Mobile Robot. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_7
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