James Richard Forbes

Google Scholar

Étudiants actuels

Timothy Everett Adams

Maîtrise recherche - McGill

Google Scholar

Angad Bajwa

Maîtrise recherche - McGill

Pierre Chamoun

Maîtrise recherche - McGill

Arturo Del Castillo Bernal

Doctorat - McGill

Mohammad Ghali

Maîtrise recherche - McGill

John Jung

Doctorat - McGill

Isabel Lougheed

Maîtrise recherche - McGill

Irene Simier

Baccalauréat - McGill

Andrew Stirling

Maîtrise recherche - McGill

Google Scholar

Junha Yoo

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Derek Nowrouzezahrai

Yanting Zhou

Doctorat - McGill

Publications

MILUV: A Multi-UAV Indoor Localization dataset with UWB and Vision

Mohammed Ayman Shalaby

Nicholas Dahdah

Syed Shabbir Ahmed

Charles Champagne Cossette

Jerome Le Ny

This paper introduces MILUV, a Multi-UAV Indoor Localization dataset with UWB and Vision measurements. This dataset comprises 217 minutes of… (voir plus) flight time over 36 experiments using three quadcopters, collecting ultra-wideband (UWB) ranging data such as the raw timestamps and channel-impulse response data, vision data from a stereo camera and a bottom-facing monocular camera, inertial measurement unit data, height measurements from a laser rangefinder, magnetometer data, and ground-truth poses from a motion-capture system. The UWB data is collected from up to 12 transceivers affixed to mobile robots and static tripods in both line-of-sight and non-line-of-sight conditions. The UAVs fly at a maximum speed of 4.418 m/s in an indoor environment with visual fiducial markers as features. MILUV is versatile and can be used for a wide range of applications beyond localization, but the primary purpose of MILUV is for testing and validating multi-robot UWB- and vision-based localization algorithms. The dataset can be downloaded at https://doi.org/10.25452/figshare.plus.28386041.v1. A development kit is presented alongside the MILUV dataset, which includes benchmarking algorithms such as visual-inertial odometry, UWB-based localization using an extended Kalman filter, and classification of CIR data using machine learning approaches. The development kit can be found at https://github.com/decargroup/miluv, and is supplemented with a website available at https://decargroup.github.io/miluv/.

2026-01-08

International Journal of Robotics Research (publié)

Nonlinear Observer Design for Visual-Inertial Odometry

Mouaad Boughellaba

Abdelhamid Tayebi

Soulaimane Berkane

This paper addresses the problem of Visual-Inertial Odometry (VIO) for rigid body systems evolving in three-dimensional space. We introduce … (voir plus)a novel matrix Lie group structure, denoted SE_{3+n}(3), that unifies the pose, gravity, linear velocity, and landmark positions within a consistent geometric framework tailored to the VIO problem. Building upon this formulation, we design an almost globally asymptotically stable nonlinear geometric observer that tightly integrates data from an Inertial Measurement Unit (IMU) and visual sensors. Unlike conventional Extended Kalman Filter (EKF)-based estimators that rely on local linearization and thus ensure only local convergence, the proposed observer achieves almost global stability through the decoupling of the rotational and translational dynamics. A globally exponentially stable Riccati-based translational observer along with an almost global input-to-state stable attitude observer are designed such that the overall cascaded observer enjoys almost global asymptotic stability. This cascaded architecture guarantees robust and consistent estimation of the extended state, including orientation, position, velocity, gravity, and landmark positions, up to the VIO unobservable directions (i.e., a global translation and rotation about gravity). The effectiveness of the proposed scheme is demonstrated through numerical simulations as well as experimental validation on the EuRoC MAV dataset, highlighting its robustness and suitability for real-world VIO applications.

2025-12-31

arXiv (prépublication)

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

Miguel Saavedra-Ruiz

Steven A. Parkison

Ria Arora

Liam Paull

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from nois… (voir plus)y sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This paper introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's performance compared with established nonparametric filtering methods across simulated and real-world localization tasks.

2025-01-31

IEEE Robotics and Automation Letters (publié)

Reducing Two-Way Ranging Variance by Signal-Timing Optimization

Mohammed Ayman Shalaby

Charles Champagne Cossette

Jerome Le Ny

Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Doubl… (voir plus)e-sided two-way ranging (DS-TWR) is widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this paper, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays, which is then compared to the Cramer-Rao Lower Bound (CRLB). This is then used to formulate an optimization problem over the response delays in order to maximize the information gained from range measurements. The derived analytical variance model and optimized protocol are validated experimentally with 2 ranging UWB transceivers, where 29 million range measurements are collected.

2024-05-31

IEEE Transactions on Aerospace and Electronic Systems (publié)