王润之等:Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes
被阅读 133 次
2018-12-06
Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes
作者:Wang, RZ (Wang, Runzhi)[ 1,2 ] ; Di, KC (Di, Kaichang)[ 1 ] ; Wan, WH (Wan, Wenhui)[ 1 ] ; Wang, YK (Wang, Yongkang)[ 3 ]
SENSORS
卷: 18  期: 10
文献号: 3559
DOI: 10.3390/s18103559
出版年: OCT 2018
 
摘要
In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary featurespoint and line segmentshave been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.
 
通讯作者地址: Wan, WH (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, 20A Datun Rd, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, 20A Datun Rd, Beijing 100101, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
[ 3 ] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China