Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Dr Michael Lau


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


In this paper, we present a novel algorithm for odometry estimation based on ceiling vision. The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches. The principal direction is defined based on the fact that our ceiling is filled with artificial vertical and horizontal lines which can be used as reference for the current robot’s heading direction. The 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 clouds. Iterative closest point (ICP) is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one. However, its performance suffers from data association problem or it requires pre-alignment information. The performance of the proposed principal direction detection approach does not rely on data association knowledge. Using this method, two point clouds are properly pre-aligned. Hence, we can use ICP to fine-tune the transformation parameters and minimize registration error. Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time. Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to significantly improve the accuracy of simultaneous localization and mapping (SLAM).

Publication metadata

Author(s): Wang H, Mou W, Seet G, Li MH, Lau MWS, Wang DW

Publication type: Article

Publication status: Published

Journal: International Journal of Automation and Computing

Year: 2013

Volume: 10

Issue: 5

Pages: 397-404

Print publication date: 01/11/2013

ISSN (print): 1476-8186

ISSN (electronic): 1751-8520

Publisher: Springer


DOI: 10.1007/s11633-013-0736-7


Altmetrics provided by Altmetric