Luca Di Giammarino — accepted AI-conference papers

2026

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

Krzysztof Cwian, Luca Di Giammarino, Simone Ferrari, Thomas Alessandro Ciarfuglia, Giorgio Grisetti, Piotr Skrzypczynski

ICRA 2026poster

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, with structure refinement either omitted or treated separately using implicit representations. This paper introduces a…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

ActLoc: Learning to Localize on the Move via Active Viewpoint Selection

Jiajie Li, Boyang Sun, Luca Di Giammarino, Hermann Blum, Marc Pollefeys

CoRL 2025poster

Reliable localization is critical for robot navigation, yet many existing systems assume that all viewpoints along a trajectory are equally informative. In practice, localization becomes unreliable when the robot observes unmapped, ambiguous, or uninformative regions. To address this, we present Act…

Cited by 0 Source ↗Scholar ↗

2025

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

Krzysztof Cwian, Luca Di Giammarino, Simone Ferrari, Thomas A. Ciarfuglia, Giorgio Grisetti, Piotr Skrzypczynski

RA-L 2025

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, with structure refinement either omitted or treated separately using implicit representations. This paper introduces a

Cited by 3 Source ↗Scholar ↗

2025

Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping

Emanuele Giacomini, Luca Di Giammarino, Lorenzo De Rebotti, Giorgio Grisetti, Martin R. Oswald

ICCV 2025poster

LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks.Although its success, managing an accurate and lightweight representation of the environment still poses challenges.Both classic and NeRF-based solutions have to trade off accuracy…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

MAD-ICP: It is All About Matching Data - Robust and Informed LiDAR Odometry

Simone Ferrari, Luca Di Giammarino, Leonardo Brizi, Giorgio Grisetti

RA-L 2024

LiDAR odometry is the task of estimating the ego-motion of the sensor from sequential laser scans. This problem has been addressed by the community for more than two decades, and many effective solutions are available nowadays. Most of these systems implicitly rely on assumptions about the operating

Cited by 25 Source ↗code ↗Scholar ↗

2024

VBR: A Vision Benchmark in Rome

Leonardo Brizi, Emanuele Giacomini, Luca Di Giammarino, Simone Ferrari, Omar Salem, Lorenzo De Rebotti +1

ICRA 2024poster

This paper presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing…

Cited by 8 Source ↗code ↗Scholar ↗

2023

Photometric LiDAR and RGB-D Bundle Adjustment

Luca Di Giammarino, Emanuele Giacomini, Leonardo Brizi, Omar Salem, Giorgio Grisetti

RA-L 2023

The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images

Cited by 10 Source ↗code ↗Scholar ↗

2022

HiPE: Hierarchical Initialization for Pose Graphs

Tiziano Guadagnino, Luca Di Giammarino, Giorgio Grisetti

RA-L 2022

Pose graph optimization is a non-convex optimization problem encountered in many areas of robotics perception. Its convergence to an accurate solution is conditioned by two factors: the non-linearity of the cost function in use and the initial configuration of the pose variables. In this letter, we

Cited by 6 Source ↗code ↗Scholar ↗

2022

MD-SLAM: Multi-cue Direct SLAM

Luca Di Giammarino, Leonardo Brizi, Tiziano Guadagnino, Cyrill Stachniss, Giorgio Grisetti

IROS 2022poster

Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are…

Cited by 14 Source ↗code ↗Scholar ↗

2021

Visual Place Recognition using LiDAR Intensity Information

Luca Di Giammarino, Irvin Aloise, Cyrill Stachniss, Giorgio Grisetti

IROS 2021poster

Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure a…

Cited by 35 Source ↗Scholar ↗