Loop closure detection problem is an essential issue in simultaneous localization and mapping (SLAM) problem. In particular, visual loop closure detection, which using a visual sensor, should be robust to environmental conditions like confusion caused by repeated structures. In this paper, we propose a robust visual loop closure detection algorithm through restrained repetitive features observed in repeating structures. The proposed algorithm aims to extract bag of visual words (BoVW) for each image frame with RootSIFT extraction, improve it by restrain dominantly repetitive features, calculates histogram similarity score with histogram comparing method and finally decides loop closure pair(s). Experimental results show that the proposed algorithm robustly performs loop closure detection.
|Title of host publication||2018 15th International Conference on Ubiquitous Robots, UR 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2018 Aug 20|
|Event||15th International Conference on Ubiquitous Robots, UR 2018 - Honolulu, United States|
Duration: 2018 Jun 27 → 2018 Jun 30
|Name||2018 15th International Conference on Ubiquitous Robots, UR 2018|
|Other||15th International Conference on Ubiquitous Robots, UR 2018|
|Period||18/6/27 → 18/6/30|
Bibliographical noteFunding Information:
This work was supported by the Industrial Convergence Core Technology Development Program (No. 10063172, Development of robot intelligence technology for mobility with learning capability toward robust and seamless indoor and outdoor autonomous navigation) funded by the Ministry of Trade, industry & Energy (MOTIE), Korea.
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Control and Optimization
- Mechanical Engineering