| Full text | |
| Author(s): |
Gama, Gabriel Soares
;
Rosa, Nicolas dos Santos
;
Grassi Jr, Valdir
;
Homem, TPD
;
Bianchi, RAD
;
DaSilva, BMF
;
Curvelo, CDF
;
Pinto, MF
Total Authors: 8
|
| Document type: | Journal article |
| Source: | 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. |
| Abstract | |
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) | |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 21/08117-0 - Development of a visual semantic descriptor applied to simultaneous localization and mapping |
| Grantee: | Gabriel Soares Gama |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |