Miaolong, Y., Ning, L., Jie, Z., & Zhenbiao, W. (2023, August 18). Visual Place Recognition Using an Unsupervised CNN Approach. 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA). https://doi.org/10.1109/iciea58696.2023.10241438
Abstract:
Visual place recognition (VPR) plays a key role in
many applications, such as mobile robot navigation and localization,
location intelligence in smart warehouse etc. Convolutional
neural network (CNN) based deep learning (DL) solutions
have been demonstrated to outperform traditional bag-of-words
(BoWs) solutions for VPR. However, CNN-based DL solutions
often require a large amount of high-quality annotated data,
where manual annotation is time-consuming and even difficult in
some scenarios. In this paper, we propose an unsupervised CNN
approach for VPR. First, we use Oriented FAST and Rotated
BRIEF (ORB) feature detector, bag-of-words (BoWs) and Hough
space verification methods to automatically generate image clusters.
Each image cluster is assigned with a numbered keyframe
representing one place. Images relating to its keyframes will be
stored as a visual experience with relevant available information,
such as pose of current keyframe, in the corresponding cluster.
Next, the image clusters are used as training datasets to train
various CNN models for VPR. Experiments have been conducted
on a public dataset and competitive results have been achieved
using the proposed unsupervised CNN method.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done