LTS-NET: End-to-End Unsupervised Learning of Long-Term 3D Stable Objects
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44981-9_2
Reference24 articles.
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2. Hong, Z., Petillot, Y., Wallace, A., Wang, S.: Radarslam: A robust simultaneous localization and mapping system for all weather conditions. Int. J. Robot. Res. 02783649221080483 (2022)
3. Arora, M., Wiesmann, L., Chen, X., Stachniss, C.: Mapping the static parts of dynamic scenes from 3d lidar point clouds exploiting ground segmentation. In: 2021 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2021)
4. Lim, H., Hwang, S., Myung, H.: Erasor: egocentric ratio of pseudo occupancy-based dynamic object removal for static 3d point cloud map building. IEEE Robot. Autom. Lett. 6(2), 2272–2279 (2021)
5. Schauer, J., Nüchter, A.: The peopleremover-removing dynamic objects from 3-d point cloud data by traversing a voxel occupancy grid. IEEE Robot. Autom. Lett. 3(3), 1679–1686 (2018)
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