1 research outputs found
Autonomous Removal of Perspective Distortion for Robotic Elevator Button Recognition
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