LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects

• URL: http://arxiv.org/abs/2301.03426v3
• Date: Mon, 12 Jun 2023 07:18:39 GMT
• Title: LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects
• Authors: Ibrahim Hroob, Sergi Molina, Riccardo Polvara, Grzegorz Cielniak and Marc Hanheide
• Abstract summary: We present an end-to-end data-driven pipeline for determining the long-term stability of objects within a given environment, specifically distinguishing between static and dynamic objects. Our pipeline includes a labelling method that utilizes historical data from the environment to generate training data for a neural network. 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 stability vs dynamic object classification.
• Score: 7.491472577165315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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