Loop closure detection in simultaneous localization and mapping using descriptor from generative adversarial network

Abstract

The loop closure detection (LCD) problem in simultaneous localization and mapping has been an important research topic to reconstruct three-dimensional environments and estimate a camera trajectory accurately. Although the bag-of-visual-word (BoVW) scheme has been used widely and has shown good results, it has several problems in some aspects, such as occlusion, deformation, illumination change, and viewpoint change. In order to tackle the challenges, we propose an LCD method using the BoVW method with a local patch descriptor obtained from the learning-based approach. We have trained a neural network model with a place-oriented dataset and extract the descriptors for the local patches from the trained neural network model. In addition, we have constructed the ground-truth label for the evaluation. Our experiment shows promising results, compared to the state-of-the-art LCD method. The implementation of the proposed deep convolutional generative adversarial network descriptor, as well as the evaluation toolbox, can be found online at https://github.com/JustWon/DCGAN_Descriptor.