Papers
7
Total Citations
59
H-Index
4
About
Tiago Barros is a researcher at the forefront of mobile robotics, specializing in localization, place recognition, and autonomous navigation. His work bridges deep learning and robotics, with key contributions in 3D LiDAR-based place recognition and sensor fusion for agricultural and indoor environments. Barros developed AttDLNet, an attention-based deep network for 3D LiDAR place recognition (18 citations), and advanced horticultural robotics with PointNetPGAP-SLC, addressing the challenge of semi-permeable environments. He also pioneered multi-stage localization systems, combining commercial absolute indoor positioning with laser-based particle filters (11 citations), and introduced reinforcement learning for map update decisions (8 citations). His comprehensive study on multispectral image segmentation in agriculture (14 citations) further demonstrates his impact in precision agriculture. Barros’s work on RGB-D object recognition accuracy versus inference speed (3 citations) and reinforcement learning for local motion planning (2 citations) rounds out his contributions to efficient, real-time robotic navigation. With over 59 citations across his most-cited papers, Barros is shaping the future of autonomous mobile robots in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1AttDLNet: Attention-Based Deep Network for 3D LiDAR Place Recognition18 citations · 2022
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- 7Improving Local Motion Planning with a Reinforcement Learning Approach2 citations · 2020