Simulation-based lidar super-resolution for ground vehicles
Simulation-based lidar super-resolution for ground vehicles
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DOI：
10.1016/j.robot.2020.103647
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发表时间：
2020-12-01
影响因子：
4.3
通讯作者：
Englot, Brendan
中科院分区：
文献类型：
--
作者：
Shan, Tixiao;Wang, Jinkun;Englot, Brendan
我们提出了一种地面车辆在道路上行驶的激光雷达超分辨率方法，该方法完全依赖于驾驶模拟器，通过深度学习来增强物理激光雷达的表观分辨率。为了提高稀疏三维激光雷达捕获点云的分辨率，我们将该问题从三维欧几里得空间转化为二维图像空间的图像超分辨率问题，并使用深度卷积神经网络解决该问题。通过将点云投影到距离图像上，我们可以使用深度神经网络有效地提高图像的分辨率。通常，深度神经网络的训练需要大量的真实数据。我们的方法不需要任何真实世界的数据，因为我们完全使用计算机生成的数据来训练网络。因此，该方法在理论上适用于任何类型的三维激光雷达的增强。通过在网络中新颖地应用蒙特卡罗dropout并去除具有高不确定性的预测，我们的方法产生了可与真实高分辨率激光雷达观测相媲美的高精度点云。我们给出了将我们的方法应用于几个模拟和现实世界数据集的实验结果。我们认为该方法在现实世界机器人应用中的潜在优势，如占用映射和地形建模。(C) 2020 Elsevier B.V.版权所有
We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the point cloud captured by a sparse 3D lidar, we convert this problem from 3D Euclidean space into an image super-resolution problem in 2D image space, which is solved using a deep convolutional neural network. By projecting a point cloud onto a range image, we are able to efficiently enhance the resolution of such an image using a deep neural network. Typically, the training of a deep neural network requires vast real-world data. Our approach does not require any real-world data, as we train the network purely using computer-generated data. Thus our method is applicable to the enhancement of any type of 3D lidar theoretically. By novelly applying Monte-Carlo dropout in the network and removing the predictions with high uncertainty, our method produces high accuracy point clouds comparable with the observations of a real high resolution lidar. We present experimental results applying our method to several simulated and real-world datasets. We argue for the method's potential benefits in real-world robotics applications such as occupancy mapping and terrain modeling. (C) 2020 Elsevier B.V. All rights reserved.