Autonomous Removal of Perspective Distortion for Robotic Elevator Button Recognition
This work addresses a domain-specific challenge in robotic elevator operation, offering an incremental improvement in robustness over traditional feature-based methods.
The paper tackles the problem of perspective distortion in elevator button recognition for autonomous robots by presenting a novel algorithm that autonomously corrects distortions using Gaussian Mixture Model-based grid fitting and camera motion estimation, achieving accurate distortion removal as validated on a dataset of 50 images.
Elevator button recognition is considered an indispensable function for enabling the autonomous elevator operation of mobile robots. However, due to unfavorable image conditions and various image distortions, the recognition accuracy remains to be improved. In this paper, we present a novel algorithm that can autonomously correct perspective distortions of elevator panel images. The algorithm first leverages the Gaussian Mixture Model (GMM) to conduct a grid fitting process based on button recognition results, then utilizes the estimated grid centers as reference features to estimate camera motions for correcting perspective distortions. The algorithm performs on a single image autonomously and does not need explicit feature detection or feature matching procedure, which is much more robust to noises and outliers than traditional feature-based geometric approaches. To verify the effectiveness of the algorithm, we collect an elevator panel dataset of 50 images captured from different angles of view. Experimental results show that the proposed algorithm can accurately estimate camera motions and effectively remove perspective distortions.