基于点云与图像交叉融合的道路分割方法

Point cloud-image data fusion for road segmentation

道路检测是车辆实现自动驾驶的前提。近年来,基于深度学习的多源数据融合成为当前自动驾驶研究的一个热点。本文采用卷积神经网络对激光雷达点云和图像数据加以融合,实现对交通场景中道路的分割。本文提出了像素级、特征级和决策级多种融合方案,尤其是在特征级融合中设计了四种交叉融合方案,对各种方案进行对比研究,给出最佳融合方案。在网络构架上,采用编码解码结构的语义分割卷积神经网络作为基础网络,将点云法线特征与RGB图像特征在不同的层级进行交叉融合。融合后的数据进入解码器还原,最后使用激活函数得到检测结果。实验使用KITTI数据集进行评估,验证了各种融合方案的性能,实验结果表明,本文提出的融合方案E具有最好的分割性能。与其他道路检测方法的比较实验表明,本文方法可以获得较好的整体性能。

Road detection is the premise of vehicle automatic driving. In recent years, multi-modal data fusion based on deep learning has become a hot spot in the research of automatic driving. In this paper, convolutional neural network is used to fuse LiDAR point cloud and image data to realize road segmentation in traffic scenes. In this paper,a variety of fusion schemes at pixel level, feature level and decision level are proposed. Especially, four cross-fusion schemes are designed in feature level fusion. Various schemes are compared, and the best fusion scheme is given.In the network architecture, the semantic segmentation convolutional neural network with encoding and decoding structure is used as the basic network to cross-fuse the point cloud normal features and RGB image features at different levels. The fused data is restored by the decoder, and finally the detection results are obtained by using the activation function. The substantial experiments have been conducted on public KITTI data set to evaluate the performance of various fusion schemes. The results show that the fusion scheme E proposed in this paper has the best segmentation performance. Compared with other road-detection methods, our method gives better overall performance.