摘要
单目视觉同步定位与地图创建(SLAM)算法的计算复杂度较高,难以满足实时处理的要求。为解决该问题,提出一种SLAM的优化算法。使用FAST特征点提取环境特征,对于每一个特征点构造BRIEF描述子,以提高算法执行效率,通过引入1-point随机抽样一致算法对算法的框架进行改进,降低算法的计算复杂度,实现视觉SLAM算法的实时处理。实验结果表明,在相机速度为30 f/s的情况下,该算法能满足实时性要求。
Monocular Simultaneous Localization and Mapping（SLAM） is computational complexity,and it is hard to reach real-time process.In order to solve this problem,this paper proposes an SLAM optimization algorithm.It uses the Feature from Accelerated Segment Test（FAST） corner to extract the environment featrures,and makes the Binary Robust Independent Elementary Feature（BRIEF） descriptor for every feature points,and improves the execution efficiency of the algorithm.1-point Random Sample Consensus（RANSAC） algorithm is introduced to improve the algorithm framework,to reduce the computational complexity of algorithm and to achieve real-time processing of visual SLAM.Experimental results show that when the camera speed is 30 frames per second,the proposed algorithm can meet the real-time requirements.
出处
《计算机工程》
CAS
CSCD
2013年第8期231-234,238,共5页
Computer Engineering
基金
国家自然科学基金资助项目(60975065)
关键词
同步定位与地图创建
扩展卡尔曼滤波
FAST角点
BRIEF描述子
随机抽样一致
单目视觉
Simultaneous Localization and Mapping（SLAM）
Extended Kalman Filtering（EKF）
FAST corner
BRIEF descriptor
Random Sample Consensus（RANSAC）
monocular vision
