most citedMAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to Structure Optimization

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2026

Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM

Davide Ceriola, Simone Ferrari, Luca Di Giammarino +2

Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-suited for handling heterogeneo…

cs.RO20262 cited

MAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to Structure Optimization

Krzysztof Ćwian, Luca Di Giammarino, Simone Ferrari +3

The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization…

cs.GR2025

Resolution Where It Counts: Hash-based GPU-Accelerated 3D Reconstruction via Variance-Adaptive Voxel Grids

Lorenzo De Rebotti, Emanuele Giacomini, Giorgio Grisetti +1

Efficient and scalable 3D surface reconstruction from range data remains a core challenge in computer graphics and vision, particularly in real-time and resource-constrained scenar…

cs.RO2025

DRO: Doppler-Aware Direct Radar Odometry

Cedric Le Gentil, Leonardo Brizi, Daniil Lisus +3

A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see' through thin walls…

cs.RO2025

Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping

Emanuele Giacomini, Luca Di Giammarino, Lorenzo De Rebotti +2

LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks. Although its success, managing an accurate and lightweight…