11 citations · 11 across the 2 of their papers we have counts for
7 papers
DigiForest: Digital Analytics and Robotics for Sustainable Forestry
Marco Camurri, Enrico Tomelleri, Matías Mattamala +18
Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and woodlands reach approximately…
OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS
Simon Boche, Jaehyung Jung, Sebastián Barbas Laina +1
To empower mobile robots with usable maps as well as highest state estimation accuracy and robustness, we present OKVIS2-X: a state-of-the-art multi-sensor Simultaneous Localizatio…
FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment
Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou +4
Geometrically accurate and semantically expressive map representations have proven invaluable for robot deployment and task planning in unknown environments. Nevertheless, real-tim…
REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation
Débora N. P. Oliveira, Joshua Knights, Sebastián Barbas Laina +3
Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point cloud…
Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras
Sotiris Papatheodorou, Simon Boche, Sebastián Barbas Laina +1
Autonomous exploration of unknown space is an essential component for the deployment of mobile robots in the real world. Safe navigation is crucial for all robotics applications an…
Uncertainty-Aware Visual-Inertial SLAM with Volumetric Occupancy Mapping
Jaehyung Jung, Simon Boche, Sebastián Barbas Laina +1
We propose visual-inertial simultaneous localization and mapping that tightly couples sparse reprojection errors, inertial measurement unit pre-integrals, and relative pose factors…