Publications

2024

1. T-RO

   

   Multi-Robot Relative Pose Estimation and IMU Preintegration Using Passive UWB Transceivers

   Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny, and James R. Forbes

   IEEE Transactions on Robotics (T-RO), 2024

   Abstract arXiv PDF Video

   Ultra-wideband (UWB) systems are becoming increasingly popular as a means of inter-robot ranging and communication. A major constraint associated with UWB is that only one pair of UWB transceivers can range at a time to avoid interference, hence hindering the scalability of UWB-based localization. In this article, a ranging protocol is proposed that allows all robots to passively listen on neighboring communicating robots without any hierarchical restrictions on the role of the robots. This is utilized to allow each robot to obtain more range measurements and to broadcast preintegrated inertial measurement unit (IMU) measurements for relative extended pose state estimation directly on SE2(3). Consequently, a simultaneous clock-synchronization and relative-pose estimator is formulated using an on-manifold extended Kalman filter (EKF) and is evaluated in simulation using Monte Carlo runs for up to seven robots. The ranging protocol is implemented in C on custom-made UWB boards fitted to three quadcopters, and the proposed filter is evaluated over multiple experimental trials, yielding up to 48% improvement in localization accuracy.

2. IJRR

   

   Decentralized State Estimation: An Approach Using Pseudomeasurements and Preintegration

   Charles C. Cossette, Mohammed A. Shalaby, David Saussié, and James R. Forbes

   The International Journal of Robotics Research (IJRR), 2024

   Abstract arXiv PDF

   This paper addresses the problem of decentralized, collaborative state estimation in robotic teams. In particular, this paper considers problems where individual robots estimate similar physical quantities, such as each other’s position relative to themselves. The use of pseudomeasurements is introduced as a means of modeling such relationships between robots’ state estimates and is shown to be a tractable way to approach the decentralized state estimation problem. Moreover, this formulation easily leads to a general-purpose observability test that simultaneously accounts for measurements that robots collect from their own sensors, as well as the communication structure within the team. Finally, input preintegration is proposed as a communication-efficient way of sharing odometry information between robots, and the entire theory is appropriate for both vector-space and Lie-group state definitions. To overcome the need for communicating preintegrated covariance information, a deep autoencoder is proposed that reconstructs the covariance information from the inputs, hence further reducing the communication requirements. The proposed framework is evaluated on three different simulated problems, and one experiment involving three quadcopters.

3. Ph.D. Thesis

   

   Ultra-wideband for Robot Navigation: Calibration, Communication, and State Estimation

   Mohammed A. Shalaby

   Ph.D. Thesis, McGill University, 2024

   Abstract PDF

   This thesis examines the utilization of ultra-wideband (UWB) radio for robot navigation in indoor applications. UWB, a wireless communication technology, offers a means of generating distance (or range) measurements and establishing communication channels among mobile robots and fixed anchors at known locations. When fixed anchors are present, UWB can be harnessed for precise localization, enabling, for example, the tracking of robots in warehouse applications. In unexplored environments lacking a fixed infrastructure, UWB transceivers installed on different robots facilitate inter-robot ranging and communication, thereby enabling relative localization - an essential prerequisite for tasks such as maintaining a group formation, collaboratively mapping an area, or ensuring effective collision avoidance. In order to maximize the potential of UWB for localization purposes, it becomes imperative to tackle fundamental challenges of UWB, such as clock synchronization and the choice of ranging protocol, concurrently with the development of the state estimation algorithm. Conventionally, these two issues have been addressed separately.The primary objective of this thesis is to resolve both the underlying low-level UWB ranging and communication performance challenges, as well as the state estimation problem, ultimately providing a practical, robust, and implementable solution for indoor navigation. To this end, this thesis delves into the matter of UWB measurement calibration, aiming to enhance ranging accuracy and characterize measurement uncertainty for use in a probabilistic framework. Subsequently, the selection of an appropriate ranging protocol is motivated to facilitate localization and efficient inter-robot communication. Lastly, this thesis employs UWB measurements within a filtering framework to tackle the problem of state estimation, encompassing both a scenario involving a single robot performing a closed-loop teach-and-repeat experiment and a multi-robot scenario where the robots perform on-manifold relative localization and attitude estimation using preintegration techniques. Comprehensive testing of all proposed algorithms is conducted through simulation and real-world experiments employing custom-built UWB modules fitted onto quadcopters.

4. TAES

   

   Reducing two-way ranging variance by signal-timing optimization

   Mohammed A. Shalaby, Charles C. Cossette, James R. Forbes, and Jerome Le Ny

   IEEE Transactions on Aerospace and Electronic Systems (TAES), 2024

   Abstract arXiv PDF

   Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Double-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 article, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays, which is then compared with the Cramer–Rao lower bound . 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 two ranging ultrawideband transceivers, where 29 million range measurements are collected.

5. CCTA

   

   Gaussian-Sum Filter for Range-based 3D Relative Pose Estimation in the Presence of Ambiguities

   Syed Shabbir Ahmed, Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny, and James R. Forbes

   in IEEE Conference on Control Technology and Applications (CCTA), 2024

   Abstract arXiv PDF

   Multi-robot systems must have the ability to accurately estimate relative states between robots in order to perform collaborative tasks, possibly with no external aiding. Three-dimensional relative pose estimation using range measurements oftentimes suffers from a finite number of non-unique solutions, or ambiguities. This paper: 1) identifies and accurately estimates all possible ambiguities in 2D; 2) treats them as components of a Gaussian mixture model; and 3) presents a computationally-efficient estimator, in the form of a Gaussian-sum filter (GSF), to realize range-based relative pose estimation in an infrastructure-free, 3D, setup. This estimator is evaluated in simulation and experiment and is shown to avoid divergence to local minima induced by the ambiguous poses. Furthermore, the proposed GSF outperforms an extended Kalman filter, demonstrates similar performance to the computationally-demanding particle filter, and is shown to be consistent.

6. RA-L/IROS

   

   DIVE: Deep Inertial-Only Velocity Aided Estimation for Quadrotors

   Angad Bajwa, Charles C. Cossette, Mohammed A. Shalaby, and James R. Forbes

   IEEE Robotics and Automation Letters (RA-L). Also appears at IROS, 2024

   Abstract PDF Code

   This letter presents a novel deep-learning-based solution to the problem of quadrotor inertial navigation. Visual-inertial odometry (VIO) is often used for quadrotor pose estimation, where an inertial measurement unit (IMU) provides a motion prior. When VIO fails, IMU dead reckoning is often used, which quickly leads to significant pose estimation drift. Learned inertial odometry leverages deep learning and model-based filtering to improve upon dead reckoning. Efforts for quadrotors, however, rely on sensors other than, or in addition to, an IMU, or have only been proven on a specific set of trajectories. The proposed generalizable approach regresses a 3D velocity estimate from only a history of IMU measurements, and the learned outputs are applied as a correction to an on-manifold Extended Kalman Filter. The proposed algorithm is shown to be superior to the state-of-the-art in learned inertial odometry. A 42% improvement in localization accuracy is shown over the state-of-the-art on an in-distribution testing set, and a 22% improvement is shown on an out-of-distribution testing set. Additionally, the proposed algorithm shows a 43% improvement over dead reckoning in VIO failure scenarios.

7. IROS

   

   Optimal Robot Formations: Balancing Range-Based Observability and User-Defined Configurations

   Syed Shabbir Ahmed, Mohammed A. Shalaby, Jerome Le Ny, and James R. Forbes

   in IEEE International Conference on Intelligent Robots and Systems (IROS), 2024

   Abstract arXiv PDF

   This paper introduces a set of customizable and novel cost functions that enable the user to easily specify desirable robot formations, such as a “high-coverage” infrastructure-inspection formation, while maintaining high relative pose estimation accuracy. The overall cost function balances the need for the robots to be close together for good ranging-based relative localization accuracy and the need for the robots to achieve specific tasks, such as minimizing the time taken to inspect a given area. The formations found by minimizing the aggregated cost function are evaluated in a coverage path planning task in simulation and experiment, where the robots localize themselves and unknown landmarks using a simultaneous localization and mapping algorithm based on the extended Kalman filter. Compared to an optimal formation that maximizes ranging-based relative localization accuracy, these formations significantly reduce the time to cover a given area with minimal impact on relative pose estimation accuracy.

2023

1. Preprint

   

   STAR-loc: Dataset for STereo And Range-based localization

   Frederike Dümbgen, Mohammed A. Shalaby, Connor Holmes, Charles C. Cossette, James R. Forbes, Jerome Le Ny, and Timothy D. Barfoot

   2023

   Abstract arXiv PDF Code

   This document contains a detailed description of the STAR-loc dataset. For a quick starting guide please refer to the associated Github repository (https://github.com/utiasASRL/starloc). The dataset consists of stereo camera data (rectified/raw images and inertial measurement unit measurements) and ultra-wideband (UWB) data (range measurements) collected on a sensor rig in a Vicon motion capture arena. The UWB anchors and visual landmarks (Apriltags) are of known position, so the dataset can be used for both localization and Simultaneous Localization and Mapping (SLAM).

2. IROS

   

   navlie: A Python Package for State Estimation on Lie Groups

   Charles C. Cossette, Mitchell Cohen, Vassili Korotkine, Arturo DC. Bernal, Mohammed A. Shalaby, and James R. Forbes

   in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

   Abstract arXiv PDF Code

   The ability to rapidly test a variety of algorithms for an arbitrary state estimation task is valuable in the prototyping phase of navigation systems. Lie group theory is now mainstream in the robotics community, and hence estimation prototyping tools should allow state definitions that belong to manifolds. A new package, called navlie, provides a framework that allows a user to model a large class of problems by implementing a set of classes complying with a generic interface. Once accomplished, navlie provides a variety of on-manifold estimation algorithms that can run directly on these classes. The package also provides a built-in library of common models, as well as many useful utilities. The open-source project can be found at https://github.com/decargroup/navlie.

3. Preprint

   

   Multi-Robot IMU Preintegration in the Presence of Bias and Communication Constraints

   Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny, and James R. Forbes

   2023

   Abstract arXiv PDF

   This document is in supplement to the paper titled "Multi-Robot Relative Pose Estimation and IMU Preintegration Using Passive UWB Transceivers", available at [1]. The purpose of this document is to show how IMU biases can be incorporated into the framework presented in [1], while maintaining the differential Sylvester equation form of the process model.

4. ICRA

   

   Calibration and Uncertainty Characterization for Ultra-Wideband Two-Way-Ranging Measurements

   Mohammed A. Shalaby, Charles C. Cossette, James R. Forbes, and Jerome Le Ny

   in IEEE International Conference on Robotics and Automation (ICRA), 2023

   Abstract arXiv PDF Video Code

   Ultra-Wideband (UWB) systems are becoming increasingly popular for indoor localization, where range measurements are obtained by measuring the time-of-flight of radio signals. However, the range measurements typically suffer from a systematic error or bias that must be corrected for high-accuracy localization. In this paper, a ranging protocol is proposed alongside a robust and scalable antenna-delay calibration procedure to accurately and efficiently calibrate antenna delays for many UWB tags. Additionally, the bias and uncertainty of the measurements are modelled as a function of the received-signal power. The full calibration procedure is presented using experimental training data of 3 aerial robots fitted with 2 UWB tags each, and then evaluated on 2 test experiments. A localization problem is then formulated on the experimental test data, and the calibrated measurements and their modelled uncertainty are fed into an extended Kalman filter (EKF). The proposed calibration is shown to yield an average of 46% improvement in localization accuracy. Lastly, the paper is accompanied by an open-source UWB-calibration Python library, which can be found at https://github.com/decargroup/uwb_calibration.

2022

1. Preprint

   

   Ultra-Wideband Teach and Repeat

   Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny, and James R. Forbes

   2022

   Abstract arXiv PDF Video

   Autonomously retracing a manually-taught path is desirable for many applications, and Teach and Repeat (T&R) algorithms present an approach that is suitable for long-range autonomy. In this paper, ultra-wideband (UWB) ranging-based T&R is proposed for vehicles with limited resources. By fixing single UWB transceivers at far-apart unknown locations in an indoor environment, a robot with 3 UWB transceivers builds a locally consistent map during the teach pass by fusing the range measurements under a custom ranging protocol with an on-board IMU and height measurements. The robot then uses information from the teach pass to retrace the same trajectory autonomously. The proposed ranging protocol and T&R algorithm are validated in simulation, where it is shown that the robot can successfully retrace the taught trajectory with sub-metre tracking error.

2. IROS

   

   Optimal multi-robot formations for relative pose estimation using range measurements

   Charles C. Cossette, Mohammed A. Shalaby, David Saussié, Jerome Le Ny, and James R. Forbes

   in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022

   Abstract arXiv PDF

   In multi-robot missions, relative position and attitude information between robots is valuable for a variety of tasks such as mapping, planning, and formation control. In this paper, the problem of estimating relative poses from a set of inter-robot range measurements is investigated. Specifically, it is shown that the estimation accuracy is highly dependent on the true relative poses themselves, which prompts the desire to find multi-robot formations that provide the best estimation performance. By direct maximization of Fischer information, it is shown in simulation and experiment that large improvements in estimation accuracy can be obtained by optimizing the formation geometry of a team of robots.

2021

1. RA-L/ICRA

   

   Cascaded Filtering Using the Sigma Point Transformation

   Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny, and James R. Forbes

   IEEE Robotics and Automation Letters (RA-L). Also appears at ICRA, 2021

   "Best Student Paper" finalist at ICRA 2021.

   Abstract arXiv PDF Video

   It is often convenient to separate a state estimation task into smaller “local” tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.

2. RA-L/ICRA

   

   Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements

   Mohammed A. Shalaby, Charles C. Cossette, James R. Forbes, and Jerome Le Ny

   IEEE Robotics and Automation Letters (RA-L). Also appears at ICRA, 2021

   Abstract arXiv PDF Video

   The ability to accurately estimate the position of robotic agents relative to one another, in possibly GPS-denied environments, is crucial to execute collaborative tasks. Inter-agent range measurements are available at a low cost, due to technologies such as ultra-wideband radio. However, the task of three-dimensional relative position estimation using range measurements in multi-agent systems suffers from unobservabilities. This letter presents a sufficient condition for the observability of the relative positions, and satisfies the condition using a simple framework with only range measurements, an accelerometer, a rate gyro, and a magnetometer. The framework has been tested in simulation and in experiments, where 40-50 cm positioning accuracy is achieved using inexpensive off-the-shelf hardware.

3. RA-L/IROS

   

   Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes

   Daniil Lisus, Charles C. Cossette, Mohammed A. Shalaby, and James R. Forbes

   IEEE Robotics and Automation Letters (RA-L). Also appears at IROS, 2021

   Abstract arXiv PDF Blog

   It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers are common in indoor localization problems. This letter experimentally demonstrates how to use UWB range and received signal strength (RSS) measurements to estimate robot heading. The RSS of a UWB antenna varies with its orientation. As such, a Gaussian process (GP) is used to learn a data-driven relationship from UWB range and RSS inputs to orientation outputs. Combined with a gyroscope in an invariant extended Kalman filter, this realizes a heading estimation method that uses only UWB and gyroscope measurements.

4. RA-L/ICRA

   

   Relative position estimation between two UWB devices with IMUs

   Charles C. Cossette, Mohammed A. Shalaby, David Saussié, James R. Forbes, and Jerome Le Ny

   IEEE Robotics and Automation Letters (RA-L). Also appears at ICRA, 2021

   Abstract arXiv PDF

   For a team of robots to work collaboratively, it is crucial that each robot have the ability to determine the position of their neighbors, relative to themselves, in order to execute tasks autonomously. This letter presents an algorithm for determining the three-dimensional relative position between two mobile robots, each using nothing more than a single ultra-wideband transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding window filter estimates the relative position at selected keypoints by combining the distance measurements with acceleration estimates, which each agent computes using an on-board attitude estimator. The algorithm is appropriate for real-time implementation, and has been tested in simulation and experiment, where it comfortably outperforms standard estimators. A positioning accuracy of less than 1 m is achieved with inexpensive sensors.

5. IROS

   

   Localization with Directional Coordinates

   Charles C. Cossette, Mohammed A. Shalaby, David Saussié, and James R. Forbes

   in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

   Abstract arXiv PDF

   A coordinate system is proposed that replaces the usual three-dimensional Cartesian x, y, z position coordinates, for use in robotic localization applications. Range, azimuth, and elevation measurement models become greatly simplified, and, unlike spherical coordinates, the proposed coordinates do not suffer from the same kinematic singularities and angle wraparound. When compared to Cartesian coordinates, the proposed coordinate system results in a significantly enhanced ability to represent the true distribution of robot positions, ultimately leading to large improvements in state estimation consistency.