基于双目图像的大尺度智能驾驶场景重建

摘要/Abstract

摘要： 大尺度智能驾驶场景重建能够在车辆驾驶环境中为车辆控制系统反馈周围道路交通的环境信息,并实现环境信息的可视化。目前,现有的三维重建方案主要面向结构化场景,对大尺度非结构化的智能驾驶场景进行三维重建时,在保证一定精度的情况下,难以满足智能驾驶系统所需的实时性。针对这一问题,文中提出了一种基于双目视觉的三维场景重建方法,首先通过优化立体匹配策略来提高立体匹配效率,再提出均匀保距的特征点提取算法RSD,减少三维点云计算与三角剖分耗时,提高大尺度智能驾驶场景重建的实时性。实验结果证明了该算法的有效性,采用所提算法对大尺度智能驾驶场景进行场景重建可得到良好的重建效果,且能够满足智能驾驶系统对实时性的需求。

关键词: 立体匹配, 深度值计算, 双目视觉, 特征提取, 智能驾驶场景重建

Abstract: The large-scale smart driving scene reconstruction can feedback the surrounding road traffic environment information for the vehicle control system in the vehicle driving environment,and realize the visualization of the environmental information.At present,the existing three-dimensional reconstruction scheme is mainly oriented to thestructuredscene,and it is difficult to meet the real-time performance required by the smart driving system while ensuring a certain precision which can make when the three-dimensional reconstruction of the large-scale unstructured smart driving scene is performed.In order to solve this problem,a three-dimensional scene reconstruction method based on binocular vision is proposed.Firstly,by optimizing the stereo matching strategy,the stereo matching efficiency is improved,and then the uniform distance feature point extraction algorithm RSD is proposed to reduce the time consumption of 3D point cloud computing and triangulation,and the real-time performance of large-scale smart driving scene reconstruction is improved.The experimental results prove the effectiveness of this algorithm,which can be used to reconstruct the scene of large-scale smart driving scene,and can meet the demand of intelligent driving system in real-time.

Key words: Binocular vision, Depth value calculation, Feature extraction, Intelligent driving scene reconstruction, Stereo matching

中图分类号:

TP391.41

李银国, 周中奎, 白羚. 基于双目图像的大尺度智能驾驶场景重建[J]. 计算机科学, 2019, 46(11A): 251-254. https://doi.org/

LI Yin-guo, ZHOU Zhong-kui, BAI Ling. Large-scale Automatic Driving Scene Reconstruction Based on Binocular Image[J]. Computer Science, 2019, 46(11A): 251-254. https://doi.org/

参考文献

[1]谢志萍,雷莉萍.智能网联汽车环境感知技术的发展和研究现状[J].成都工业学院学报,2016,19(4):87-92.
[2]李克强,戴一凡,李升波,等.智能网联汽车(ICV)技术的发展现状及趋势[J].汽车安全与节能学报,2017,8(1):1-14.
[3]ZHANG X Y,GAO H B,GUO M,et al.A study on key technologies of unmanned driving[J].CAAI Transactions on Intelligence Technology,2016,1(1):4-13.
[4]陈辉,马世伟,Andreas Nuechter.基于激光扫描和SFM的非同步点云三维重构方法[J].仪器仪表学报,2016,37(5):1148-1157.
[5]徐超,李乔.基于计算机视觉的三维重建技术综述[J].数字技术与应用,2017,1(34):54-56.
[6]孙宇阳.基于单幅图像的三维重建技术综述[J].北方工业大学学报,2011,23(1):9-13.
[7]黄鹏程,江剑宇,杨波.双目立体视觉的研究现状及进展[J].光学仪器,2018,40(4):81-86.
[8]BAYKANT B,ALAGO Z.Obtaining Depth Maps FromColorImages By Region Based Stereo Matching Algorithms[J].OncuBilim Algorithm And Systems Labs,2008,8(4):122-134.
[9]何人杰.双目立体视觉区域局部匹配算法的改进及其实现[J].现代电子技术,2009,32(12):68-70.
[10]肖志涛,卢晓方,耿磊,等.基于极线校正的亚像素相位立体匹配方法[J].红外与激光工程,2014,43(S1):225-230.
[11]YU L,ZHANG D R, HOLDEN E J.A fast and fully automatic registration approach based on point features for multi-source remote-sensing images[J].Computers and Geosciences,2007,34(7):838-848.
[12]HARRIS C,STEPHENS M J.A combined corner and edge detector[C]∥Proceedingsof Fourth Alvey Vision Conference.Manchester.England:IEEE,1998:147-151.
[13]LOWE D G.Distinctive Image Features from Scale-InvariantKeypoints[J].International Journal of Computer Vision,2004,60(2):92-109.
[14]BAY H,ESS A,TUYTELAARS T,et al.Speeded-Up Robust Features (SURF)[J].Computer Vision and Image Understanding,2007,110(3):346-359.
[15]RUBLEE E, RABAUD V, KONOLIGE K,et al.ORB:an efficient alternative to SIFT or SURF[C]∥IEEE International Conference on Computer Vision.2011:2564-2571.
[16]MUJAM,LOWE D G.Fast approximate nearest neighborswith automaticalgorithm configuration[C]∥Proceedingsof IEEE Conference on Computer Vision Theory and Applications.Lisbon,Portugal:IEEE Computer Society,2009:331-340.
[17]CANDÉS E J,ROMBERG J K,TAO T.Stable signal recovery from incomplete and inaccurate measurements[J].Communications on Pure and Applied Mathematics,2006,59(8):1-15.
[18]吕耀文,康凯.基于双目视觉的三维重建和拼接技术研究[J].光电子技术,2016,36(4):237-241.
[19]汪神岳,刘强,王超然,等.基于双目立体相机的室外场景三维重建系统设计[J].计算机测量与控,2017,25(11):137-140.
[20]谢增广.平面点集Delaunay三角剖分的分治算法[J].计算机工程与设计,2012,33(7):2652-2658.
[21]姜翰青,王博胜,章国锋,等.面向复杂三维场景的高质量纹理映射[J].计算机学报,2015,38(12):2349-2360.

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