摘要
研究了无人驾驶车面向校园环境的同时定位与地图构建问题.采用双目视觉系统进行立体视觉图像匹配,并以此为基础完成优化前的位姿拓扑地图构建;采用了一种基于ORB图像特征和BoW模型的闭环检测算法,并利用时间连续性约束和几何一致性约束来提升闭环匹配正确率.位姿拓扑地图的后端优化采用了高斯-牛顿优化方法,并且在迭代过程中充分考虑了系统信息矩阵的稀疏性.利用实验室自主研发的SmartCruiser无人驾驶车平台在校园环境进行了实验,结果验证了本文所提方法的有效性和实用性.
Simultaneous localization and mapping (SLAM)problem for unmanned ground vehicles (UGVs)in campus environments was investigated.A stereo image matching algorithm was deployed to perform consistent pose estimation so that an initial pose-graph model was constructed.A loop clo-sure detection algorithm based on oriented FAST and Rotated BRIEF (ORB)feature matching and bag-of-words (BOW)model was utilized in our work,which can provide the constraints of temporal consistency and of geometrical consistency to improve the accuracy of loop closure.The back-end im-plementation for graph-based SLAM was used the Gaussian-Newton optimization method and the sparse characteristics of the system information matrix was fully utilized in the iterative procedure. The experiments were conducted on our self-developed UGV in DUT campus,and the results show the validity and robust performance of the proposed approach.
出处
《华中科技大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2015年第S1期319-323,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
"十二五"国家科技支撑计划重点资助项目(2013BAK03B01)
关键词
双目视觉
闭环检测
同时定位与地图构建
拓扑图优化
无人驾驶车
binocular camera
loop closure detection
simultaneous localization and mapping(SLAM)
optimization of topological graph
unmanned ground vehicle(UGV)