In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360{\deg} range images in real time.
翻译:在本文中,我们描述了对神经网络进行培训的战略,以便利用LiDAR传感器不同类型LiDAR传感器的标签数据,在从一种类型的LiDAR传感器获得的射程图像中进行天体探测。此外,还介绍了一个用于自驾驶汽车的射程图像中进行天体探测的有效模型。目前,LiDAR测量天体探测的最高演算法以神经网络为基础。利用监督的学习进行这些网络培训需要大量的附加说明的数据集。因此,使用神经网络从LIDAR点云中探测天体探测天体的多数研究是在极少数公开的数据集中进行的。因此,只使用少量的传感器类型。我们使用现有的附加说明的数据集来培训一个神经网络,该神经网络的分辨率比记录附加说明的数据集低。这是通过根据更高分辨率数据集模拟低分辨率LIDAR传感器的数据来完成的。此外,使用LIDAR范围图像探测天体探测天体的模型和从实际的分辨率低分辨率传感器到研究的图像都显示。