Search Results for author: François Pomerleau

Found 10 papers, 5 papers with code

Training Deep Learning Algorithms on Synthetic Forest Images for Tree Detection

1 code implementation8 Oct 2022 Vincent Grondin, François Pomerleau, Philippe Giguère

In this work, we propose to use simulated forest environments to automatically generate 43 k realistic synthetic images with pixel-level annotations, and use it to train deep learning algorithms for tree detection.

object-detection Object Detection +2

Instance Segmentation for Autonomous Log Grasping in Forestry Operations

1 code implementation3 Mar 2022 Jean-Michel Fortin, Olivier Gamache, Vincent Grondin, François Pomerleau, Philippe Giguère

Using our dataset, we then compare three neural network architectures on the task of individual logs detection and segmentation; two region-based methods and one attention-based method.

Inductive Bias Instance Segmentation +1

Analysis of Robust Functions for Registration Algorithms

no code implementations2 Oct 2018 Philippe Babin, Philippe Giguère, François Pomerleau

However, without a large scale comparison of solutions to filter outliers, it is becoming tedious to select an appropriate algorithm for a given application.

Robotics

CELLO-3D: Estimating the Covariance of ICP in the Real World

no code implementations2 Oct 2018 David Landry, François Pomerleau, Philippe Giguère

The fusion of Iterative Closest Point (ICP) reg- istrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty.

Robotics

Driving Datasets Literature Review

no code implementations26 Oct 2019 Charles-Éric Noël Laflamme, François Pomerleau, Philippe Giguère

This report is a survey of the different autonomous driving datasets which have been published up to date.

Autonomous Driving

Improving the Iterative Closest Point Algorithm using Lie Algebra

no code implementations21 Oct 2020 Maxime Vaidis, Johann Laconte, Vladimír Kubelka, François Pomerleau

Applications that require accurate maps, such as environmental monitoring, benefit from additional sensor modalities that reduce such drift.

Robotics

Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments

no code implementations8 Mar 2021 Johann Laconte, Elie Randriamiarintsoa, Abderrahim Kasmi, François Pomerleau, Roland Chapuis, Christophe Debain, Romuald Aufrère

While navigating in complex urban environments, the Bayesian occupancy grid is one of the most popular types of maps, where the information of occupancy is stored as the probability of collision.

Autonomous Vehicles Robotics 68T40

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