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Giorgio Grisetti

39 accepted 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 0Source ↗PDF ↗Scholar ↗
2025

CAD2SLAM: Adaptive Projection Between CAD Blueprints and SLAM Maps

Martín Bayón-Gutiérrez, Natalia Prieto-Fernández, María Teresa García-Ordás, José Alberto Benítez-Andrades, Héctor Alaiz-Moretón, Giorgio Grisetti
RA-L 2025

Robotic mobile platforms are key building blocks for numerous applications and cooperation between robots and humans is a key aspect to enhance productivity and reduce labor cost. To operate safely, robots typically rely on a custom map of the environment that depends on the sensor configuration of

Cited by 0Source ↗Scholar ↗
2025

DRO: Doppler-Aware Direct Radar Odometry with Gyroscope

Cedric Le Gentil, Leonardo Brizi, Daniil Lisus, Xinyuan Qiao, Giorgio Grisetti, Timothy Barfoot
RSS 2025poster

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, vegetation, and adversarial weather conditions such as heavy rain, fog, snow, and dust. In this paper, we propose a no…

Cited by 0PDF ↗Scholar ↗
2025

KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities

Tiziano Guadagnino, Benedikt Mersch, Saurabh Gupta, Ignacio Vizzo, Giorgio Grisetti, Cyrill Stachniss
IROS 2025

Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later

Cited by 17Source ↗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 3Source ↗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 0Source ↗PDF ↗code ↗Scholar ↗
2024

Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information

Luca Di Giammarino*, Boyang Sun, Giorgio Grisetti, Marc Pollefeys, Hermann Blum, Daniel Barath
ECCV 2024poster

"Accurate localization in diverse environments is a fundamental challenge in computer vision and robotics. The task involves determining a sensor’s precise position and orientation, typically a camera, within a given space. Traditional localization methods often rely on passive sensing, which may st…

Cited by 1Source ↗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 25Source ↗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 8Source ↗code ↗Scholar ↗
2023

Handling Constrained Optimization in Factor Graphs for Autonomous Navigation

Barbara Bazzana, Tiziano Guadagnino, Giorgio Grisetti
RA-L 2023

Factor graphs are graphical models used to represent a wide variety of problems across robotics, such as Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM) and calibration. Typically, at their core, they have an optimization problem whose terms only depend on a small subset of

Cited by 16Source ↗Scholar ↗
2023

On Domain-Specific Pre- Training for Effective Semantic Perception in Agricultural Robotics

Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino, Jan Weyler, Giorgio Grisetti, Cyrill Stachniss +1
ICRA 2023poster

Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic…

Cited by 5Source ↗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 10Source ↗code ↗Scholar ↗
2022

DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments

Louis Wiesmann, Tiziano Guadagnino, Ignacio Vizzo, Giorgio Grisetti, Jens Behley, Cyrill Stachniss
RA-L 2022

Reliable and accurate registration of point clouds is a challenging problem in robotics as well as in the domain of autonomous driving. In this article, we address the task of aligning point clouds with low overlap, containing moving objects, and without prior information about the initial guess. We

Cited by 14Source ↗Scholar ↗
2022

Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images

Tiziano Guadagnino, Xieyuanli Chen, Matteo Sodano, Jens Behley, Giorgio Grisetti, Cyrill Stachniss
RA-L 2022

Ego-motion estimation is a fundamental building block of any autonomous system that needs to navigate in an environment. In large-scale outdoor scenes, 3D LiDARs are often used for this task, as they provide a large number of range measurements at high precision. In this paper, we propose a novel ap

Cited by 25Source ↗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 14Source ↗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 35Source ↗Scholar ↗
2020

Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use

Mirco Colosi, Irvin Aloise, Tiziano Guadagnino, Dominik Schlegel, Bartolomeo Della Corte, Kai O. Arras +1
IROS 2020poster

Simultaneous Localization and Mapping (SLAM) is considered a mature research field with numerous applications and publicly available open-source systems. Despite this maturity, existing SLAM systems often rely on ad-hoc implementations or are tailored to predefined sensor setups. In this work, we ta…

Cited by 18Source ↗Scholar ↗
2019

Active SLAM using Connectivity Graphs as Priors

Alberto Soragna, Marco Baldini, Dominik Joho, Rainer Kümmerle, Giorgio Grisetti
IROS 2019poster

Mobile robots can be considered completely autonomous if they embed active algorithms for Simultaneous Localization And Mapping (SLAM). This means that the robot is able to autonomously, or actively, explore and create a reliable map of the environment, while simultaneously estimating its pose. In t…

Cited by 18Source ↗Scholar ↗
2019

Better Lost in Transition Than Lost in Space: SLAM State Machine

Mirco Colosi, Sebastian Haug, Peter Biber, Kai O. Arras, Giorgio Grisetti
IROS 2019poster

A Simultaneous Localization and Mapping (SLAM) system is a complex program consisting of several interconnected components with different functionalities such as optimization, tracking or loop detection. Whereas the literature addresses in detail how enhancing the algorithmic aspects of the individu…

Cited by 6Source ↗Scholar ↗
2019

Systematic Handling of Heterogeneous Geometric Primitives in Graph-SLAM Optimization

Irvin Aloise, Bartolomeo Della Corte, Federico Nardi, Giorgio Grisetti
RSS 2019poster

In this paper, we propose a pose-landmark graph optimization back-end that supports maps consisting of points, lines or planes. Our back-end allows representing both homogeneous (point-point, line-line, plane-plane) and heterogeneous measurements (point-on-line, point-on-plane, line-on-plane). Rathe…

Cited by 11Source ↗PDF ↗Scholar ↗
2019

Systematic Handling of Heterogeneous Geometric Primitives in Graph-SLAM Optimization

Irvin Aloise, Bartolomeo Della Corte, Federico Nardi, Giorgio Grisetti
RA-L 2019

In this letter, we propose a pose-landmark graph optimization back-end that supports maps consisting of points, lines, or planes. Our back-end allows representing both homogeneous (point-point, line-line, plane-plane) and heterogeneous measurements (point-on-line, point-on-plane, line-on-plane). Rat

Cited by 8Source ↗Scholar ↗
2019

Unified Motion-Based Calibration of Mobile Multi-Sensor Platforms With Time Delay Estimation

Bartolomeo Della Corte, Henrik Andreasson, Todor Stoyanov, Giorgio Grisetti
RA-L 2019

The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it, and their time de

Cited by 32Source ↗Scholar ↗
2019

Unified Representation and Registration of Heterogeneous Sets of Geometric Primitives

Federico Nardi, Bartolomeo Della Corte, Giorgio Grisetti
RA-L 2019

Registering models is an essential building block of many robotic applications. In case of three-dimensional data, the models to be aligned usually consist of point clouds. In this letter, we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, i

Cited by 11Source ↗Scholar ↗
2018

A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

Bartolomeo Della Corte, Igor Bogoslavskyi, Cyrill Stachniss, Giorgio Grisetti
ICRA 2018poster

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple diff…

Cited by 35Source ↗Scholar ↗
2018

An Effective Multi-Cue Positioning System for Agricultural Robotics

Marco Imperoli, Ciro Potena, Daniele Nardi, Giorgio Grisetti, Alberto Pretto
RA-L 2018

The self-localization capability is a crucial component for Unmanned Ground Vehicles in farming applications. Approaches based solely on visual cues or on a low-cost Global Positioning System (GPS) are easily prone to fail in such scenarios. In this letter, we present a robust and accurate three-dim

Cited by 41Source ↗Scholar ↗
2018

HBST: A Hamming Distance Embedding Binary Search Tree for Feature-Based Visual Place Recognition

Dominik Schlegel, Giorgio Grisetti
RA-L 2018

Reliable and efficient visual place recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this letter, we present a Hamming distance embedding binary search tree (HBST) approach for binary descriptor matching and image retrieval. HBST allows for descriptor se

Cited by 43Source ↗Scholar ↗
2017

Automatic model based dataset generation for fast and accurate crop and weeds detection

Maurilio Di Cicco, Ciro Potena, Giorgio Grisetti, Alberto Pretto
IROS 2017poster

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models…

Cited by 192Source ↗Scholar ↗