Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites
Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites
复制标题
使用轻型深度神经网络架构进行实时场景分割，用于建筑工地上的自主机器人导航
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Edgar J. Lobaton
中科院分区：
文献类型：
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作者：
Khashayar Asadi;Pengyu Chen;Kevin K. Han;Tianfu Wu;Edgar J. Lobaton
配备摄像头的无人驾驶车辆（UV）在施工监测应用的数据收集方面受到了广泛关注。为了开发自主平台，UV应该能够处理多个模块（例如，上下文感知、控制、定位和映射）。逐像素语义分割为UV提供了上下文感知其周围环境的能力。然而，在具有有限计算资源的移动的机器人系统的情况下，分割模型的大尺寸和高存储器使用需要高计算资源，这对于移动的UV（例如，具有有限有效载荷和空间的小型飞行器）。为了克服这一挑战，本文提出了一种在嵌入式平台上实时运行的轻量级高效深度神经网络架构。所提出的模型分割图像序列上的可导航空间（即，视频流），这对于基于机器视觉的自主车辆是必不可少的。结果表明，与现有模型相比，所提出的架构的性能效率，并建议可能的改进，可以使模型更有效，这是必要的自主机器人系统的未来发展。
Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules (e.g., context-awareness, control, localization, and mapping) on an embedded platform. Pixel-wise semantic segmentation provides a UV with the ability to be contextually aware of its surrounding environment. However, in the case of mobile robotic systems with limited computing resources, the large size of the segmentation model and high memory usage requires high computing resources, which a major challenge for mobile UVs (e.g., a small-scale vehicle with limited payload and space). To overcome this challenge, this paper presents a light and efficient deep neural network architecture to run on an embedded platform in real-time. The proposed model segments navigable space on an image sequence (i.e., a video stream), which is essential for an autonomous vehicle that is based on machine vision. The results demonstrate the performance efficiency of the proposed architecture compared to the existing models and suggest possible improvements that could make the model even more efficient, which is necessary for the future development of the autonomous robotics systems.