Tese: Visual SLAM in Dynamic Environments using Panoptic Segmentation
Aluno(a) : Gabriel Fischer AbatiOrientador(a): Marco A. Meggiolaro e João Carlos Soares
Área de Concentração: Mecânica Aplicada
Data: 17/05/2023
Link para tese/dissertação: https://doi.org/10.17771/PUCRio.acad.63634
Resumo: The majority of visual SLAM systems are not robust in dynamic scenarios. The ones that deal with dynamic content in the scenes usually rely on deep learning-based methods to detect and filter dynamic objects. However, these methods cannot deal with unknown objects. This work presents a visual SLAM system robust to dynamic environments, even in the presence of unknown objects. It uses Panoptic Segmentation filter dynamic objectsfrom the scene during the state estimation process. The proposed methodology is based on ORB-SLAM3, a state-of-the-art SLAM system for static environments. The implementation was tested using real- world datasets and compared with several systems from the literature, including DynaSLAM, DS-SLAM and SaD-SLAM. Link da defesa: https://puc-rio.zoom.us/j/97387225930?pwd=SDFmenJ5SWthYXFLVkVWZWVKbG1SZz09