Zobrazeno 1 - 10
of 183
pro vyhledávání: '"Pomerleau, François"'
We propose a novel method to enhance the accuracy of the Iterative Closest Point (ICP) algorithm by integrating altitude constraints from a barometric pressure sensor. While ICP is widely used in mobile robotics for Simultaneous Localization and Mapp
Externí odkaz:
http://arxiv.org/abs/2410.00758
Autor:
Fortin, Jean-Michel, Gamache, Olivier, Fecteau, William, Daum, Effie, Larrivée-Hardy, William, Pomerleau, François, Giguère, Philippe
Terrain awareness is an essential milestone to enable truly autonomous off-road navigation. Accurately predicting terrain characteristics allows optimizing a vehicle's path against potential hazards. Recent methods use deep neural networks to predict
Externí odkaz:
http://arxiv.org/abs/2409.18253
This report presents a wearable plug-and-play platform for data acquisition in the field. The platform, extending a waterproof Pelican Case into a 20 kg backpack offers 5.5 hours of power autonomy, while recording data with two cameras, a lidar, an I
Externí odkaz:
http://arxiv.org/abs/2405.00199
Recent advances in autonomous driving for uncrewed ground vehicles (UGVs) have spurred significant development, particularly in challenging terrains. This paper introduces a classification system assessing various UGV deployments reported in the lite
Externí odkaz:
http://arxiv.org/abs/2405.00189
In this paper, we present a field report of the mapping of the Athabasca Glacier, using a custom-made lidar-inertial mapping platform. With the increasing autonomy of robotics, a wider spectrum of applications emerges. Among these, the surveying of e
Externí odkaz:
http://arxiv.org/abs/2404.18790
Autor:
Boxan, Matěj, Krawciw, Alexander, Daum, Effie, Qiao, Xinyuan, Lilge, Sven, Barfoot, Timothy D., Pomerleau, François
In this paper, we propose the FoMo (For\^et Montmorency) dataset: a comprehensive, multi-season data collection. Located in the Montmorency Forest, Quebec, Canada, our dataset will capture a rich variety of sensory data over six distinct trajectories
Externí odkaz:
http://arxiv.org/abs/2404.13166
Autor:
LaRocque, Damien, Guimont-Martin, William, Duclos, David-Alexandre, Giguère, Philippe, Pomerleau, François
Recent works in field robotics highlighted the importance of resiliency against different types of terrains. Boreal forests, in particular, are home to many mobility-impeding terrains that should be considered for off-road autonomous navigation. Also
Externí odkaz:
http://arxiv.org/abs/2403.16877
Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions
Autor:
Deschênes, Simon-Pierre, Baril, Dominic, Boxan, Matěj, Laconte, Johann, Giguère, Philippe, Pomerleau, François
We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localization And Mapping (SLAM) algorithms against gyroscope saturations induced by aggressive motions. Field robotics expose robots to various hazards,
Externí odkaz:
http://arxiv.org/abs/2310.07844
Autor:
Gamache, Olivier, Fortin, Jean-Michel, Boxan, Matěj, Vaidis, Maxime, Pomerleau, François, Giguère, Philippe
Visual Odometry (VO) is one of the fundamental tasks in computer vision for robotics. However, its performance is deeply affected by High Dynamic Range (HDR) scenes, omnipresent outdoor. While new Automatic-Exposure (AE) approaches to mitigate this h
Externí odkaz:
http://arxiv.org/abs/2309.13139
Autor:
Vaidis, Maxime, Shahraji, Mohsen Hassanzadeh, Daum, Effie, Dubois, William, Giguère, Philippe, Pomerleau, François
Numerous datasets and benchmarks exist to assess and compare Simultaneous Localization and Mapping (SLAM) algorithms. Nevertheless, their precision must follow the rate at which SLAM algorithms improved in recent years. Moreover, current datasets fal
Externí odkaz:
http://arxiv.org/abs/2309.11935