Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots

被引:35
|
作者
Zhang, Xinzheng [2 ]
Rad, Ahmad B. [1 ]
Wong, Yiu-Kwong [3 ]
机构
[1] Simon Fraser Univ, Sch Engn Sci, 250-13450,102 Ave, Surrey, BC V3T 0A3, Canada
[2] Jinan Univ, Sch Elect & Informat Engn, Zhuhai 519070, Guangdong, Peoples R China
[3] Hong Kong Polytech Univ, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China
关键词
feature fusion; multi-sensor point estimation fusion (MPEF); homography transform matrix; SLAM;
D O I
10.3390/s120100429
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.
引用
收藏
页码:429 / 452
页数:24
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