An Online Visual Loop Closure Detection Method for Indoor Robotic Navigation

Erhan C., Sariyanidi E., Sencan O., Temeltaş H.

Conference on Intelligent Robots and Computer Vision XXXII - Algorithms and Techniques, San-Francisco, Costa Rica, 9 - 10 February 2015, vol.9406 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 9406
  • Doi Number: 10.1117/12.2082532
  • City: San-Francisco
  • Country: Costa Rica
  • Istanbul Technical University Affiliated: Yes


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.