Visual Odometry through Appearance- and Feature-Based Method with Omnidirectional Images

IF 1.4 Q4 ROBOTICS Journal of Robotics Pub Date : 2012-09-06 DOI:10.1155/2012/797063
David García, L. F. Rojo, A. G. Aparicio, L. P. Castelló, Ó. R. García
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引用次数: 27

Abstract

In the field of mobile autonomous robots, visual odometry entails the retrieval of a motion transformation between two consecutive poses of the robot by means of a camera sensor solely. A visual odometry provides an essential information for trajectory estimation in problems such as Localization and SLAM (Simultaneous Localization and Mapping). In this work we present a motion estimation based on a single omnidirectional camera. We exploited the maximized horizontal field of view provided by this camera, which allows us to encode large scene information into the same image. The estimation of the motion transformation between two poses is incrementally computed, since only the processing of two consecutive omnidirectional images is required. Particularly, we exploited the versatility of the information gathered by omnidirectional images to perform both an appearance-based and a feature-based method to obtain visual odometry results. We carried out a set of experiments in real indoor environments to test the validity and suitability of both methods. The data used in the experiments consists of a large sets of omnidirectional images captured along the robot's trajectory in three different real scenarios. Experimental results demonstrate the accuracy of the estimations and the capability of both methods to work in real-time.
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基于全向图像的基于外观和特征的视觉里程计
在移动自主机器人领域,视觉里程计需要仅通过相机传感器检索机器人两个连续姿态之间的运动变换。视觉里程计为定位和SLAM(同时定位和映射)等问题的轨迹估计提供了必要的信息。在这项工作中,我们提出了一种基于单个全向相机的运动估计方法。我们利用了这台相机提供的最大水平视野,这使我们能够将大型场景信息编码到同一张图像中。由于只需要处理两个连续的全向图像,因此两个姿态之间的运动变换的估计是增量计算的。特别是,我们利用全向图像收集的信息的多功能性来执行基于外观和基于特征的方法来获得视觉里程计结果。我们在真实的室内环境中进行了一组实验,以检验这两种方法的有效性和适用性。实验中使用的数据包括在三种不同的真实场景中沿着机器人轨迹捕获的大量全方位图像。实验结果证明了估计的准确性和两种方法的实时性。
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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