David Rapado Rincon — accepted AI-conference papers

2026

AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM

Mirko Usuelli, David Rapado Rincon, Gert Kootstra, Matteo Matteucci

RA-L 2026

Autonomous robots in orchards require real-time 3D scene understanding despite repetitive row geometry, seasonal appearance changes, and wind-driven foliage motion. We present AgriGS-SLAM, a Visual–LiDAR SLAM framework that couples direct LiDAR odometry and loop closures with multi-camera 3D Gaussia

Cited by 1 Source ↗code ↗Scholar ↗

2023

Multi-Hypothesis Tracking in a Graph-Based World Model for Knowledge-Driven Active Perception

Jordy Senden, Kevin Hollands, David Rapado Rincon, Akshay K. Burusa, Bas Herremans, Herman Bruyninckx +1

RA-L 2023

Robots that have to robustly execute their task in an environment containing many variations need situational awareness to adapt at run-time. This work proposes a knowledge-centered software architecture with a world model (WM) as a first class citizen, from which other software components can query

Cited by 5 Source ↗Scholar ↗