Real-time Visual Odometry Estimation Based on Principal Direction Detection on Ceiling Vision

作     者：Han Wang Wei Mou Gerald Seet Mao-Hai Li M.W.S.Lau Dan-Wei Wang 

作者机构：School of Electrical and Electronics Engineering Nanyang Technological University School of Mechanical and Aerospace Engineering Nanyang Technological University School of Mechanical and Electrical Engineering Soochow University School of Mechanical and System Engineering Newcastle University 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2013年第10卷第5期

页      面：397-404页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Visual odometry ego motion principal direction ceiling vision simultaneous localization and mapping(SLAM) 

摘      要：In this paper,we present a novel algorithm for odometry estimation based on ceiling *** main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation *** principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading *** proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera（Kinect）,mounted on a robot,is used to continuously acquire point *** closest point（ICP） is the common way to estimate the current camera position by registering the currently captured point cloud to the previous ***,its performance sufers from data association problem or it requires pre-alignment *** performance of the proposed principal direction detection approach does not rely on data association *** this method,two point clouds are properly ***,we can use ICP to fne-tune the transformation parameters and minimize registration *** results demonstrate the performance and stability of the proposed system under disturbance in *** indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping（SLAM）.