论文标题
森林机器人群的嵌入式计算机的深度估计
Depth estimation on embedded computers for robot swarms in forest
论文作者
论文摘要
迄今为止，机器人群尚未为自主导航做准备，例如森林地板的路径规划和障碍物检测，无法实现低成本。深度传感和嵌入式计算硬件的开发为大量的陆地机器人铺平了道路。这项研究的目的是通过开发低成本视觉系统来改善这种情况，以使小型地面机器人快速感知地形。我们开发了两个深度估计模型，并就深度估计模型的准确性，运行时和模型大小以及内存消耗，功耗，功率，温度和成本以及上述两个嵌入式板上计算机的上述计算机的成本来评估它们在Raspberry Pi 4和Jetson Nano上的性能。我们的研究表明，在Raspberry Pi 4上部署的自动编码器网络以3.4 W的功耗，记忆消耗约200 MB，平均运行时间为13 ms。这可以满足我们对低成本机器人的要求。此外，我们的分析还表明，多尺度的深网可以更好地预测由摄像机运动引起的模糊的RGB图像的深度图。本文主要描述在我们自己的数据集中训练的深度估计模型，以及它们在嵌入式板载计算机上的性能。
Robot swarms to date are not prepared for autonomous navigation such as path planning and obstacle detection in forest floor, unable to achieve low-cost. The development of depth sensing and embedded computing hardware paves the way for swarm of terrestrial robots. The goal of this research is to improve this situation by developing low cost vision system for small ground robots to rapidly perceive terrain. We develop two depth estimation models and evaluate their performance on Raspberry Pi 4 and Jetson Nano in terms of accuracy, runtime and model size of depth estimation models, as well as memory consumption, power draw, temperature, and cost of above two embedded on-board computers. Our research demonstrated that auto-encoder network deployed on Raspberry Pi 4 runs at a power consumption of 3.4 W, memory consumption of about 200 MB, and mean runtime of 13 ms. This can be to meet our requirement for low-cost swarm of robots. Moreover, our analysis also indicated multi-scale deep network performs better for predicting depth map from blurred RGB images caused by camera motion. This paper mainly describes depth estimation models trained on our own dataset recorded in forest, and their performance on embedded on-board computers.
