LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects
This work addresses the need for long-term stable landmarks in mobile robot localization, though it appears incremental as it builds on existing unsupervised and point cloud regression techniques.
The paper tackles the problem of distinguishing static from dynamic objects in 3D environments for mobile robot localization by proposing LTS-NET, an end-to-end unsupervised pipeline that uses point-wise continuous labels for training; results show it outperforms direct classification methods on point cloud data from parking lots in the NCLT dataset.
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.