| Texto completo | |
| Autor(es): |
Gama, Gabriel Soares
;
Rosa, Nicolas dos Santos
;
Grassi Jr, Valdir
;
Homem, TPD
;
Bianchi, RAD
;
DaSilva, BMF
;
Curvelo, CDF
;
Pinto, MF
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 LATIN AMERICAN ROBOTICS SYMPOSIUM (LARS), 2022 BRAZILIAN SYMPOSIUM ON ROBOTICS (SBR), AND 2022 WORKSHOP ON ROBOTICS IN EDUCATION (WRE); v. N/A, p. 6-pg., 2022-01-01. |
| Resumo | |
Several SLAM methods benefit from the use of semantic information. Most integrate photometric methods with high-level semantics such as object detection and semantic segmentation. We propose that adding a semantic segmentation decoder in a shared encoder architecture would help the descriptor decoder learn semantic information, improving the feature extractor. This would be a more robust approach than only using high-level semantic information since it would be intrinsically learned in the descriptor and would not depend on the final quality of the semantic prediction. To add this information, we take advantage of multi-task learning methods to improve accuracy and balance the performance of each task. The proposed models are evaluated according to detection and matching metrics on the HPatches dataset. The results show that the Semantic SuperPoint model performs better than the baseline one. (AU) | |
| Processo FAPESP: | 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente |
| Beneficiário: | Marco Henrique Terra |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 21/08117-0 - Desenvolvimento de um descritor visual semântico aplicado em tarefa de localização e mapeamento simultâneo |
| Beneficiário: | Gabriel Soares Gama |
| Modalidade de apoio: | Bolsas no Brasil - Iniciação Científica |