Lucas Teixeira

About

Lucas Teixeira is a robotics researcher whose work sits at the intersection of aerial autonomy, sensor fusion, and 3D scene understanding. His research focuses primarily on enabling Unmanned Aerial Vehicles (UAVs) to perceive, localize, and navigate intelligently in complex real-world environments — capabilities fundamental to applications ranging from search-and-rescue operations to industrial inspection and precision agriculture. Teixeira's most influential contribution, "GOMSF: Graph-Optimization Based Multi-Sensor Fusion for Robust UAV Pose Estimation" (2018, 128 citations), introduced a principled decoupled framework for fusing proprioceptive and exteroceptive sensors to achieve accurate, high-rate pose estimation — a cornerstone challenge in agile robot navigation. His work on visual-inertial localization with weak GPS priors and monocular-inertial SLAM further demonstrates his commitment to robust, deployable autonomy in GPS-degraded environments. More recently, he has advanced depth completion techniques for aerial platforms (58 citations), multi-UAV decentralized forest exploration (60 citations), and volumetric semantic mapping, pushing robots toward richer environmental understanding. Collectively, his publications have accumulated over 470 citations, reflecting sustained influence across the robotics and computer vision communities. Teixeira's body of work represents a coherent vision: building UAVs that are not merely airborne, but genuinely autonomous, perceptive, and practically deployable.

Research Focus

Key Achievements

Top Papers

1. 1
   GOMSF: Graph-Optimization Based Multi-Sensor Fusion for robust UAV Pose estimation

   128 citations · 2018
2. 2
   Fast Multi-UAV Decentralized Exploration of Forests

   60 citations · 2023
3. 3
   Aerial Single-View Depth Completion With Image-Guided Uncertainty Estimation

   58 citations · 2020
4. 4
   Robust visual-inertial localization with weak GPS priors for repetitive UAV flights

   46 citations · 2017
5. 5
   Real-Time Wide-Baseline Place Recognition Using Depth Completion

   41 citations · 2019
6. 6
   VI-RPE: Visual-Inertial Relative Pose Estimation for Aerial Vehicles

   34 citations · 2018
7. 7
   Real-time mesh-based scene estimation for aerial inspection

   29 citations · 2016
8. 8
   Robust visual-inertial localization with weak GPS priors for repetitive UAV flights

   28 citations · 2017
9. 9
   Short-term UAV path-planning with monocular-inertial SLAM in the loop

   26 citations · 2017
10. 10
    Volumetric Instance-Level Semantic Mapping Via Multi-View 2D-to-3D Label Diffusion

    24 citations · 2022

Key Collaborators

Contact & Links