In this research, we present an end-to-end data-driven pipeline for determining the long-term stability status of objects within a given environment, specifically distinguishing between static and dynamic objects. Understanding object stability is key for mobile robots since long-term stable objects can be exploited as landmarks for long-term localisation. Our pipeline includes a labelling method that utilizes historical data from the environment to generate training data for a neural network. Rather than utilizing discrete labels, we propose the use of point-wise continuous label values, indicating the spatio-temporal stability of individual points, to train a point cloud regression network named LTS-NET. Our approach is evaluated on point cloud data from two parking lots in the NCLT dataset, and the results show that our proposed solution, outperforms direct training of a classification model for static vs dynamic object classification.
@article{arxiv.2301.03426,
title = {LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects},
author = {Ibrahim Hroob and Sergi Molina and Riccardo Polvara and Grzegorz Cielniak and Marc Hanheide},
journal= {arXiv preprint arXiv:2301.03426},
year = {2023}
}