摘要
为了在道路检测中结合图像的多尺度特征以及点云的空间结构特征,使检测算法能有效地排除道路场景中的阴影、光线等干扰,本文提出一种基于融合分层条件随机场的图像和点云融合的道路分割模型.首先,利用Meanshift算法产生多个尺度的超像素分割,建立基于图像的多尺度分层条件随机场.将点云数据投影到图像平面,再建立基于点云的多尺度分层条件随机场.在条件随机场的像素层和点云层之间建立连接,构造多尺度的融合模型.然后,针对多尺度融合模型中图像层的每一层和点云层的每一层,分别提取对应尺度的图像特征或点云特征.每一层用梯度提升树算法根据提取的特征训练1个分类器,利用每一层的分类器得到对应层的数据项代价.最后,使用α扩张算法对融合模型进行联合优化求解.在KITTI Road数据集上的实验结果表明,该方法具有良好的道路检测性能.
A road segmentation model for fusing image and point cloud based on fusion via hierarchical CRF(conditional random field) is proposed, to combine the multi-scale features of image and the spatial features of point cloud in road detection, so that the interferences of shadow and light in road scene can be removed by the detection algorithm efficiently.Firstly, the Meanshift algorithm is used to generate multi-scale super-pixel segmentation, and a multi-scale hierarchical CRF based on image is created. The point cloud data of Lidar is projected to the image plane, multi-scale hierarchical CRF based on point cloud is created. And a multi-scale fusion model is created by establishing connections between the pixel layer and the point cloud layer of CRF. Then, for every pixel layer and every point cloud layer in the multi-scale fusion model, the image features and the point cloud features of the corresponding scale are extracted. A gradient boosting tree classifier is trained for every layer based on the extracted features, and the data cost of the corresponding layer is obtained by the classifier of every layer. Finally, the α-expansion algorithm is adopted to optimize the fusion model jointly. The experimental results on KITTI Road dataset show that the proposed method obtains a good performance for road detection.
出处
《机器人》
EI
CSCD
北大核心
2018年第6期803-816,共14页
Robot
基金
国家自然科学基金(61703209)
关键词
分层条件随机场
融合
道路分割
多尺度特征
空间结构特征
hierarchical conditional random field
fusion
road segmentation
multi-scale feature
spatial structure feature