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

Sensors

Xinzheng Zhang¹  Ahmad B. Rad² 

[1] School of Electrical and Information Engineering, Jinan University, Zhuhai 519070, Guangdong, China; E-Mail:;School of Engineering Science, Mechatronic System Engineering, Simon Fraser University, 250-13450, 102 Avenue, Surrey, BC, V3T 0A3, Canada

关键词: feature fusion;    multi-sensor point estimation fusion (MPEF);    homography transform matrix;    SLAM;   

DOI  :  10.3390/s120100429

来源: mdpi

【 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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland