AKAZE-Based Visual Odometry From Floor Images Supported by Acceleration Models

Abstract

To realize the self-localization of autonomous robots, methods for the 2D motion measurement of robots are required. In this research, a self-localization system using a CCD camera is proposed. In the proposed system, the self-localization is estimated by movement tracking using some keypoints detected from the floor images captured by the CCD camera. For the illumination of floor image, two LED illuminate are used. These lighting systems are installed in such a way that lit from both sides of the floor and parallel to the floor so that some minimal bumps or original veins are captured from the floor. An accelerated KAZE feature is applied in the proposed system for keypoint detection and for computing resulting in the generation of the descriptor. In the estimation, the location information from the previous step and the estimated position one frame ahead are used. Using the estimated result and computed descriptor, the proposed system matches the nearest keypoint between the estimated keypoint detected from the previous floor image and the keypoint detected from the next floor image at the beginning. The Hamming distance is employed in the proposed system to evaluate the matching. If the Hamming distance is longer than a threshold, the proposed system tries to match from the 2nd nearest keypoint. Based on an experiment in which the measurement distance and computation time are investigated, the effectiveness of the proposed method is confirmed.

Keywords

• Visual odometry
• vision measurement
• image motion analysis
• odometry measurement
• AKAZE features
• image matching