A Brain-inspired SLAM System Based on ORB Features
A Brain-inspired SLAM System Based on ORB Features
复制标题
基于ORB特征的类脑SLAM系统
DOI：
10.1007/s11633-017-1090-y
复制
发表时间：
2017-10-01
影响因子：
4.3
通讯作者：
Tang, Huajin
中科院分区：
文献类型：
--
作者：
Zhou, Sun-Chun;Yan, Rui;Tang, Huajin
针对移动的机器人，提出了一种基于加速线段测试的定向特征和RGB（red，绿色，blue）传感器的旋转二进制鲁棒独立基本（ORB）特征的脑启发同步定位与地图构建（SLAM）系统。核心SLAM系统，被称为RatSLAM，可以使用原始里程计和视觉场景的信息构建认知地图。与现有的RatSLAM系统仅使用一个简单的向量来表示视觉图像的特征不同，本文采用了一种高效且非常快速的描述符方法，称为ORB，从RGB图像中提取特征。实验表明，这些特征适合于识别熟悉的视觉场景序列。因此，当检测到环路闭合错误时，描述性特征将通过在地图校正算法中驱动环路闭合和定位来帮助修改姿态估计。通过与不同的视觉处理算法的比较，证明了该方法的有效性和鲁棒性。
This paper describes a brain-inspired simultaneous localization and mapping (SLAM) system using oriented features from accelerated segment test and rotated binary robust independent elementary (ORB) features of RGB (red, green, blue) sensor for a mobile robot. The core SLAM system, dubbed RatSLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing RatSLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RGB images. Experiments show that these features are suitable to recognize the sequences of familiar visual scenes. Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.