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

International Journal of Automation and computing 2013年第5期10卷 397-404页

作者： Han Wang Wei Mou gerald seet Mao-Hai Li M.W.S.Lau Dan-Wei Wang 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

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）.

关键词： Visual odometry ego-motion principal direction ceiling vision simultaneous localization and mapping(SLAM)

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Mobile Robot Ego Motion Estimation using RANSAC-based Ceiling Vision

Mobile Robot Ego Motion Estimation using RANSAC-based Ceilin...

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第24届中国控制与决策会议

作者： Minh Hiep Ly M.W.S. Lau gerald seet School of Electrical ＆ Electronic Engineering Nanyang Technological University

Visual odometry is a commonly used technique for recovering motion and location of the robot. In this paper, we present a robust visual odometry estimation approach based on ceiling view from 3D camera （Kinect）. We extracted Speed- up Robust Features （SURF） from the monocular image frames retrieved from the camera. SURF features from two consecutive frames are matched by finding the nearest neighbor using KD- tree. 3D information of the SURF features are retrieved using the camera’s depth map. The 3D affine transformation model is estimated between these two frames based on Random Sample Consensus （RANSAC） method. All inliers are then used to reestimate the relative transformation between two frames by Singular Value Decomposition （SVD）. Given this, the global robot position and orientation can be calculated. Experimental results demonstrate the performance of the proposed algorithm in real environments.

关键词： Visual Odometry Ceiling vision RANSAC SURF

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