Real-time visual odometry estimation based on principal direction detection on ceiling vision

  • Authors:

  • Han Wang;Wei Mou;Gerald Seet;Mao-Hai Li;M. W. Lau;Dan-Wei Wang

  • Affiliations:

  • School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore;School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore;School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China 215006;School of Mechanical and System Engineering, Newcastle University, Newcastle upon Tyne, UK NE1 7RU;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

  • Venue:
  • International Journal of Automation and Computing
  • Year:
  • 2013

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Abstract

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).