Yvan Petillot — accepted AI-conference papers

2026

VISO: Robust Underwater Visual-Inertial-Sonar SLAM with Photometric Rendering for Dense 3D Reconstruction

Shu Pan, Simon Archieri, Ahmet Fatih Cinar, Jonatan Scharff Willners, Ignacio Carlucho, Yvan Petillot

ICRA 2026poster

Visual challenges in underwater environments significantly hinder the accuracy of vision-based localisation and the high-fidelity dense reconstruction. In this paper, we propose VISO, a robust underwater SLAM system that fuses a stereo camera, an inertial measurement unit (IMU), and a 3D sonar to ac…

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

Semi-autonomous surface-tracking tasks using omnidirectional mobile manipulators

Carlos Suarez Zapico, Yvan Petillot, Mustafa Suphi Erden

ICRA 2024poster

Despite the potential of mobile manipulators and applications where robots require a force-controlled physical interaction with the environment, the majority of robot automation nowadays is still based on fixed manipulators for free-motion tasks (e.g. welding, pick and place, or painting). In this w…

Cited by 4 Source ↗Scholar ↗

2023

Large-Scale Radar Localization using Online Public Maps

Ziyang Hong, Yvan Petillot, Kaicheng Zhang, Shida Xu, Sen Wang

ICRA 2023poster

In this paper, we propose using online public maps, e.g., OpenStreetMap (OSM), for large-scale radar-based localization without needing a prior sensing map. This can potentially extend the localization system to anywhere worldwide without building, saving, or maintaining a sensing map, as long as an…

Cited by 5 Source ↗Scholar ↗

2022

Sliding Mode Controller for Positioning of an Underwater Vehicle Subject to Disturbances and Time Delays

Harun Tugal, Kamil Cetin, Xiaoran Han, Ibrahim Kucukdemiral, Joshua Roe, Yvan Petillot +1

ICRA 2022poster

Unmanned underwater vehicles are crucial for deep-sea exploration and inspection without imposing any danger to human life due to extreme environmental conditions. But, designing a robust controller that can cope with model uncertainties, external disturbances, and time delays for such vehicles is a…

Cited by 10 Source ↗Scholar ↗

2020

RadarSLAM: Radar based Large-Scale SLAM in All Weathers

Ziyang Hong, Yvan Petillot, Sen Wang

IROS 2020poster

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for…

Cited by 159 Source ↗Scholar ↗

2020

Self-Assessment of Grasp Affordance Transfer

Paola Ardón, Èric Pairet, Yvan Petillot, Ronald P. A. Petrick, Subramanian Ramamoorthy, Katrin S. Lohan

IROS 2020poster

Reasoning about object grasp affordances allows an autonomous agent to estimate the most suitable grasp to execute a task. While current approaches for estimating grasp affordances are effective, their prediction is driven by hypotheses on visual features rather than an indicator of a proposal's sui…

Cited by 21 Source ↗Scholar ↗

2019

Global Localization with Object-Level Semantics and Topology

Yu Liu, Yvan Petillot, David Lane, Sen Wang

ICRA 2019poster

Global localization lies at the heart of autonomous navigation and Simultaneous Localization and Mapping (SLAM). The appearance-based approach has been successful, but still faces many open challenges in environments where visual conditions vary significantly over time. In this paper, we propose an…

Cited by 80 Source ↗Scholar ↗

2019

TextPlace: Visual Place Recognition and Topological Localization Through Reading Scene Texts

Ziyang Hong, Yvan Petillot, David Lane, Yishu Miao, Sen Wang

ICCV 2019poster

Visual place recognition is a fundamental problem for many vision based applications. Sparse feature and deep learning based methods have been successful and dominant over the decade. However, most of them do not explicitly leverage high-level semantic information to deal with challenging scenarios…

Cited by 67 PDF ↗code ↗Scholar ↗

2017

Underwater 3D structures as semantic landmarks in SONAR mapping

Thomas Guerneve, Kartic Subr, Yvan Petillot

IROS 2017poster

SONAR mapping of underwater environments leads to dense point-clouds. These maps have large memory footprints, are inherently noisy and consist of raw data with no semantic information. This paper presents an approach to underwater semantic mapping where known man-made structures that appear in mult…

Cited by 15 Source ↗Scholar ↗