تقرير
TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation
| العنوان: | TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation |
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| المؤلفون: | Santo, Antonio, Cabrera, Juan J., Valiente, David, Viegas, Carlos, Gil, Arturo |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | This paper presents TE-NeXt, a novel and efficient architecture for Traversability Estimation (TE) from sparse LiDAR point clouds based on a residual convolution block. TE-NeXt block fuses notions of current trends such as attention mechanisms and 3D sparse convolutions. TE-NeXt aims to demonstrate high capacity for generalisation in a variety of urban and natural environments, using well-known and accessible datasets such as SemanticKITTI, Rellis-3D and SemanticUSL. Thus, the designed architecture ouperforms state-of-the-art methods in the problem of semantic segmentation, demonstrating better results in unstructured environments and maintaining high reliability and robustness in urbans environments, which leads to better abstraction. Implementation is available in a open repository to the scientific community with the aim of ensuring the reproducibility of results. Comment: This work has been submitted to the IEEE Transactions on Intelligent Vehicles for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.01395 |
| رقم الأكسشن: | edsarx.2406.01395 |
| قاعدة البيانات: | arXiv |
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