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Paulo L. J. Drews-Jr

4 accepted papers

2024

LetsMap: Unsupervised Representation Learning for Label-Efficient Semantic BEV Mapping

Nikhil Gosala*, Kürsat Petek, B Ravi Kiran, Senthil Yogamani, Paulo L. J. Drews-Jr, Wolfram Burgard +1
ECCV 2024poster

"Semantic Bird’s Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping approaches employ a fully supervised learning paradigm that relies on large amounts of human-annotated BEV ground truth…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images

Nikhil Gosala, Kürsat Petek, Paulo L. J. Drews-Jr, Wolfram Burgard, Abhinav Valada
CVPR 2023poster

Bird's-Eye-View (BEV) semantic maps have become an essential component of automated driving pipelines due to the rich representation they provide for decision-making tasks. However, existing approaches for generating these maps still follow a fully supervised training paradigm and hence rely on larg…

Cited by 39Source ↗PDF ↗Scholar ↗
2022

Cross-View and Cross-Domain Underwater Localization Based on Optical Aerial and Acoustic Underwater Images

Matheus Machado dos Santos, Giovanni G. De Giacomo, Paulo L. J. Drews-Jr, Silvia S. C. Botelho
RA-L 2022

Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between colo

Cited by 19Source ↗Scholar ↗
2020

Matching Color Aerial Images and Underwater Sonar Images Using Deep Learning for Underwater Localization

Matheus Machado dos Santos, Giovanni G. De Giacomo, Paulo L. J. Drews-Jr, Silvia S. C. Botelho
RA-L 2020

Underwater localization is a challenging task due to the lack of a Global Positioning System (GPS). However, the capability to match georeferenced aerial images and acoustic data can help with this task. Autonomous hybrid aerial and underwater vehicles also demand a new localization method capable o

Cited by 41Source ↗Scholar ↗