iBoW-LCD: An Appearance-based Loop Closure Detection Approach using Incremental Bags of Binary Words
This addresses loop closure detection for robotics and autonomous systems, offering an incremental improvement over existing appearance-based methods.
The paper tackled loop closure detection in visual SLAM by introducing iBoW-LCD, which uses incremental bags of binary words and dynamic islands to avoid vocabulary training and reduce computation, achieving high accuracy and outperforming state-of-the-art methods on public datasets.
In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure detection method. The presented approach makes use of an incremental Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of dynamic islands, a simple but effective mechanism to group similar images close in time, which reduces the computational times typically associated to Bayesian frameworks. Our approach is validated using several indoor and outdoor public datasets, taken under different environmental conditions, achieving a high accuracy and outperforming other state-of-the-art solutions.