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  • 杨晶东,郭远首*.基于联合点线特征的医疗服务机器人同时定位与地图构建算法研究[J].第二军医大学学报,2019,40(5):507-511    [点击复制]
  • YANG Jing-dong,GUO Yuan-shou*.Simultaneous localization and mapping algorithm based on point and line features for medical service robots[J].Acad J Sec Mil Med Univ,2019,40(5):507-511   [点击复制]
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基于联合点线特征的医疗服务机器人同时定位与地图构建算法研究
杨晶东,郭远首*
0
(上海理工大学光电信息与计算机工程学院自主机器人实验室, 上海 200093
*通信作者)
摘要:
目的 为提高医疗服务机器人同时定位与地图构建(SLAM)算法全局定位精度和实时性,提出基于点线特征SLAM(PL-SLAM)算法,并与ORB(oriented FAST and rotated BRIEF)-SLAM2算法进行比较。方法 PL-SLAM算法在特征提取过程中在点特征的基础上增加线段特征,根据融合后的点线特征,在复杂医疗环境内进行地图创建与全局定位。利用公开数据集EuRoc和KITTI对比PL-SLAM算法与ORB-SLAM2算法,测试医疗服务机器人的自主导航综合性能。结果 与ORB-SLAM2算法相比,PL-SLAM算法在弱纹理环境下能够提取较多的点线特征,定位精度和实时性均有较大提升。其中旋转误差较ORB-SLAM2算法减小42.2%,运算速度提高55.9%。结论 PL-SLAM算法能够有效提高医疗服务机器人全局定位精度和实时性。
关键词:  医疗服务机器人  同时定位与地图创建  ORB算法  弱纹理环境
DOI:10.16781/j.0258-879x.2019.05.0507
投稿时间:2018-11-07修订日期:2018-12-07
基金项目:国家自然科学基金(61374039),上海市自然科学基金(15ZR1429100),沪江基金(C14002).
Simultaneous localization and mapping algorithm based on point and line features for medical service robots
YANG Jing-dong,GUO Yuan-shou*
(Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
*Corresponding author)
Abstract:
Objective To propose a point and line (PL)-simultaneous localization and mapping (SLAM) algorithm and to compare it with oriented FAST and rotated BRIEF (ORB)-SLAM2, so as to improve the global localization accuracy and real-time performance of SLAM algorithm for medical service robots. Methods The PL-SLAM algorithm added line features based on point feature in the process of feature extraction, and carried out mapping and global localization in the complex medical environment according to the point and line features after fusion. The public datasets (EuRoc and KITTI) were used to compare the PL-SLAM and ORB-SLAM2 algorithms, and the comprehensive performance of autonomous navigation of the medical service robots was tested. Results Compared with the ORB-SLAM2 algorithm, PL-SLAM algorithm extracted more point and line features in weak texture scenario, and effectively enhanced the global localization accuracy and real-time performance. The rotation error of the PL-SLAM algorithm decreased by 42.2% and the runtime increased by 55.9%. Conclusion PL-SLAM algorithm can effectively improve global localization accuracy and the real-time performance of medical service robots.
Key words:  medical service robotics  simultaneous localization and mapping  oriented FAST and rotated BRIEF  weak texture scenario