In this paper, we present an enhanced loop closure method* based on image-to-image matching relies on quantized local Zernike moments. In contradistinction to the previous methods, our approach uses additional depth
information to extract Zernike moments in local manner. These moments are used to represent holistic shape
information inside the image. The moments in complex space that are extracted from both grayscale and depth
images are coarsely quantized. In order to find out the similarity between two locations, nearest neighbour (NN)
classification algorithm is performed. Exemplary results and the practical implementation case of the method
are also given with the data gathered on the testbed using a Kinect. The method is evaluated in three different
datasets of different lighting conditions. Additional depth information with the actual image increases the detection rate especially in dark environments. The results are referred as a successful, high-fidelity online method
for visual place recognition as well as to close navigation loops, which is a crucial information for the well known
simultaneously localization and mapping (SLAM) problem. This technique is also practically applicable because
of its low computational complexity, and performing capability in real-time with high loop closing accuracy.
This paper introduces a novel image description technique that aims at appearance based loop closure detection
for mobile robotics applications. This technique relies on the local evaluation of the Zernike Moments. Binary
patterns, which are referred to as Local Zernike Moment (LZM) patterns, are extracted from images, and these
binary patterns are coded using histograms. Each image is represented with a set of histograms, and loop closure
is achieved by simply comparing the most recent image with the images in the past trajectory. The technique
has been tested on the New College dataset, and as far as we know, it outperforms the other methods in terms
of computation efficiency and loop closure precision.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.