Michele Grimaldi — accepted AI-conference papers

2026

InsSo3D: Inertial Navigation System and 3D Sonar SLAM for Turbid Environment Inspection

Simon Archieri, Ahmet Fatih Cinar, Shu Pan, Scharff Willners Jonatan, Michele Grimaldi, Ignacio Carlucho +1

ICRA 2026poster

This paper presents InsSo3D, an accurate and efficient method for large-scale 3D Simultaneous Localisation and Mapping (SLAM) using a 3D Sonar and an Inertial Navigation System (INS). Unlike traditional sonar, which produces 2D images containing range and azimuth information but lacks elevation info…

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2025

3DSSDF: Underwater 3D Sonar Reconstruction Using Signed Distance Functions

Simon Archieri, Juliette Drupt, Ahmet Fatih Cinar, Michele Grimaldi, Ignacio Carlucho, Jonatan Scharff Willners +1

ICRA 2025

Underwater autonomous robotic operations require online localization and 3D mapping. Because of the absence of absolute positioning underwater, these tasks strongly rely on embedded sensors, including proprioceptive or navigation sensors - which can be fused for an odometry, - and exteroceptive sens

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2025

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation

Markus Buchholz, Ignacio Carlucho, Michele Grimaldi, Maria Koskinopoulou, Yvan R. Petillot

IROS 2025

Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments. Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings. In this paper, we int

Cited by 1 Source ↗Scholar ↗

2025

Stonefish: Supporting Machine Learning Research in Marine Robotics

Michele Grimaldi, Patryk Cieslak, Eduardo Ochoa, Vibhav Bharti, Hayat Rajani, Ignacio Carlucho +3

ICRA 2025

Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operatio

Cited by 19 Source ↗code ↗Scholar ↗

2024

FRAGG-Map: Frustum Accelerated GPU-Based Grid Map

Michele Grimaldi, Narcis Palomeras, Ignacio Carlucho, Yvan R. Petillot, Pere Ridao Rodriguez

IROS 2024poster

In robotics, occupancy grids serve as required repositories of information about the environment in numerous applications. One such critical application is Simultaneous Localization and Mapping (SLAM), where robots dynamically scan and explore their surroundings while in motion. In the context of ex…

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