arXiv subjects

François Michaud

Publications and source records attributed to François Michaud.

At least 19 recordsLinked to original sources

arXiv2024

Memory Management for Real-Time Appearance-Based Loop Closure Detection

Loop closure detection is the process involved when trying to find a match between the current and a previously visited locations in SLAM. Over time, the amount of time required to process new observations increases with the size of the internal map, which may influence real-time processing. In this paper, we present a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on a memory management method that keeps computation time for each new observation under a fixed limit. Results demonstrate the approach's adaptability and scalability using four standard data sets.

cs.RO↗

arXiv2024

Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM

For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect.

cs.RO↗

arXiv2024

Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation

In appearance-based localization and mapping, loop closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map increases, so does the time required to compare new observations with all stored locations, eventually limiting online processing. This paper presents an online loop closure detection approach for large-scale and long-term operation. The approach is based on a memory management method, which limits the number of locations used for loop closure detection so that the computation time remains under real-time constraints. The idea consists of keeping the most recent and frequently observed locations in a Working Memory (WM) used for loop closure detection, and transferring the others into a Long-Term Memory (LTM). When a match is found between the current location and one stored in WM, associated locations stored in LTM can be updated and remembered for additional loop closure detections. Results demonstrate the approach's adaptability and scalability using ten standard data sets from other appearance-based loop closure approaches, one custom data set using real images taken over a 2 km loop of our university campus, and one custom data set (7 hours) using virtual images from the racing video game ``Need for Speed: Most Wanted''.

cs.RO↗

arXiv2024

RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation

Distributed as an open source library since 2013, RTAB-Map started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement Simultaneous Localization and Mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of contraints on sensors, processing capabilities and locomotion, it raises the question of which SLAM approach is the most appropriate to use in terms of cost, accuracy, computation power and ease of integration. Since most of SLAM approaches are either visual or lidar-based, comparison is difficult. Therefore, we decided to extend RTAB-Map to support both visual and lidar SLAM, providing in one package a tool allowing users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. This paper presents this extended version of RTAB-Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real-world datasets (e.g., KITTI, EuRoC, TUM RGB-D, MIT Stata Center on PR2 robot), outlining strengths and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.

cs.RO↗

arXiv2023

Fast Cross-Correlation for TDoA Estimation on Small Aperture Microphone Arrays

This paper introduces the Fast Cross-Correlation (FCC) method for Time Difference of Arrival (TDoA) Estimation for pairs of microphones on a small aperture microphone array. FCC relies on low-rank decomposition and exploits symmetry in even and odd bases to speed up computation while preserving TDoA accuracy. FCC reduces the number of flops by a factor of 4.5 and the execution speed by factors between 3.5 and 8.3 on embedded hardware, compared to the state-of-the-art Generalized Cross-Correlation (GCC) method that relies on the Fast Fourier Transform (FFT). This improvement can provide portable microphone arrays with extended battery life and allow real-time processing on low-cost hardware.

eess.SP↗

arXiv2022

Long-Term Online Multi-Session Graph-Based SPLAM with Memory Management

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revisiting previously mapped areas that are in LTM, the mechanism can retrieve these locations and place them back in WM for online SPLAM. The approach is tested on a robot equipped with a short-range laser rangefinder and a RGB-D camera, patrolling autonomously 10.5 km in an indoor environment over 11 sessions while having encountered 139 people.

cs.RO↗

arXiv2022

Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments

For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 minute intervals during sunset using a Google Tango phone in a real apartment.

cs.RO↗

arXiv2022

ODAS: Open embeddeD Audition System

Artificial audition aims at providing hearing capabilities to machines, computers and robots. Existing frameworks in robot audition offer interesting sound source localization, tracking and separation performance, although involve a significant amount of computations that limit their use on robots with embedded computing capabilities. This paper presents ODAS, the Open embeddeD Audition System framework, which includes strategies to reduce the computational load and perform robot audition tasks on low-cost embedded computing systems. It presents key features of ODAS, along with cases illustrating its uses in different robots and artificial audition applications.

eess.AS↗

arXiv2022

SMP-PHAT: Lightweight DoA Estimation by Merging Microphone Pairs

This paper introduces SMP-PHAT, which performs direction of arrival (DoA) of sound estimation with a microphone array by merging pairs of microphones that are parallel in space. This approach reduces the number of pairwise cross-correlation computations, and brings down the number of flops and memory lookups when searching for DoA. Experiments on low-cost hardware with commonly used microphone arrays show that the proposed method provides the same accuracy as the former SRP-PHAT approach, while reducing the computational load by 39% in some cases.

cs.SD↗

arXiv2021

OpenTera: A Microservice Architecture Solution for Rapid Prototyping of Robotic Solutions to COVID-19 Challenges in Care Facilities

As telecommunications technology progresses, telehealth frameworks are becoming more widely adopted in the context of long-term care (LTC) for older adults, both in care facilities and in homes. Today, robots could assist healthcare workers when they provide care to elderly patients, who constitute a particularly vulnerable population during the COVID-19 pandemic. Previous work on user-centered design of assistive technologies in LTC facilities for seniors has identified positive impacts. The need to deal with the effects of the COVID-19 pandemic emphasizes the benefits of this approach, but also highlights some new challenges for which robots could be interesting solutions to be deployed in LTC facilities. This requires customization of telecommunication and audio/video/data processing to address specific clinical requirements and needs. This paper presents OpenTera, an open source telehealth framework, aiming to facilitate prototyping of such solutions by software and robotic designers. Designed as a microservice-oriented platform, OpenTera is an end-to-end solution that employs a series of independent modules for tasks such as data and session management, telehealth, daily assistive tasks/actions, together with smart devices and environments, all connected through the framework. After explaining the framework, we illustrate how OpenTera can be used to implement robotic solutions for different applications identified in LTC facilities and homes, and we describe how we plan to validate them through field trials.

cs.HC↗

arXiv2020

BIRD: Big Impulse Response Dataset

This paper introduces BIRD, the Big Impulse Response Dataset. This open dataset consists of 100,000 multichannel room impulse responses (RIRs) generated from simulations using the Image Method, making it the largest multichannel open dataset currently available. These RIRs can be used toperform efficient online data augmentation for scenarios that involve two microphones and multiple sound sources. The paper also introduces use cases to illustrate how BIRD can perform data augmentation with existing speech corpora.

cs.SD↗

arXiv2020

Dynamic Object Tracking and Masking for Visual SLAM

In dynamic environments, performance of visual SLAM techniques can be impaired by visual features taken from moving objects. One solution is to identify those objects so that their visual features can be removed for localization and mapping. This paper presents a simple and fast pipeline that uses deep neural networks, extended Kalman filters and visual SLAM to improve both localization and mapping in dynamic environments (around 14 fps on a GTX 1080). Results on the dynamic sequences from the TUM dataset using RTAB-Map as visual SLAM suggest that the approach achieves similar localization performance compared to other state-of-the-art methods, while also providing the position of the tracked dynamic objects, a 3D map free of those dynamic objects, better loop closure detection with the whole pipeline able to run on a robot moving at moderate speed.

cs.CV↗

arXiv2020

3D Localization of a Sound Source Using Mobile Microphone Arrays Referenced by SLAM

A microphone array can provide a mobile robot with the capability of localizing, tracking and separating distant sound sources in 2D, i.e., estimating their relative elevation and azimuth. To combine acoustic data with visual information in real world settings, spatial correlation must be established. The approach explored in this paper consists of having two robots, each equipped with a microphone array, localizing themselves in a shared reference map using SLAM. Based on their locations, data from the microphone arrays are used to triangulate in 3D the location of a sound source in relation to the same map. This strategy results in a novel cooperative sound mapping approach using mobile microphone arrays. Trials are conducted using two mobile robots localizing a static or a moving sound source to examine in which conditions this is possible. Results suggest that errors under 0.3 m are observed when the relative angle between the two robots are above 30 degrees for a static sound source, while errors under 0.3 m for angles between 40 degrees and 140 degrees are observed with a moving sound source.

eess.AS↗

arXiv2018

Cheap or Robust? The Practical Realization of Self-Driving Wheelchair Technology

To date, self-driving experimental wheelchair technologies have been either inexpensive or robust, but not both. Yet, in order to achieve real-world acceptance, both qualities are fundamentally essential. We present a unique approach to achieve inexpensive and robust autonomous and semi-autonomous assistive navigation for existing fielded wheelchairs, of which there are approximately 5 million units in Canada and United States alone. Our prototype wheelchair platform is capable of localization and mapping, as well as robust obstacle avoidance, using only a commodity RGB-D sensor and wheel odometry. As a specific example of the navigation capabilities, we focus on the single most common navigation problem: the traversal of narrow doorways in arbitrary environments. The software we have developed is generalizable to corridor following, desk docking, and other navigation tasks that are either extremely difficult or impossible for people with upper-body mobility impairments.

cs.RO↗

arXiv2016

Microphone array post-filter for separation of simultaneous non-stationary sources

Microphone array post-filters have demonstrated their ability to greatly reduce noise at the output of a beamformer. However, current techniques only consider a single source of interest, most of the time assuming stationary background noise. We propose a microphone array post-filter that enhances the signals produced by the separation of simultaneous sources using common source separation algorithms. Our method is based on a loudness-domain optimal spectral estimator and on the assumption that the noise can be described as the sum of a stationary component and of a transient component that is due to leakage between the channels of the initial source separation algorithm. The system is evaluated in the context of mobile robotics and is shown to produce better results than current post-filtering techniques, greatly reducing interference while causing little distortion to the signal of interest, even at very low SNR.

cs.SD↗

arXiv2016

Enhanced Robot Audition Based on Microphone Array Source Separation with Post-Filter

We propose a system that gives a mobile robot the ability to separate simultaneous sound sources. A microphone array is used along with a real-time dedicated implementation of Geometric Source Separation and a post-filter that gives us a further reduction of interferences from other sources. We present results and comparisons for separation of multiple non-stationary speech sources combined with noise sources. The main advantage of our approach for mobile robots resides in the fact that both the frequency-domain Geometric Source Separation algorithm and the post-filter are able to adapt rapidly to new sources and non-stationarity. Separation results are presented for three simultaneous interfering speakers in the presence of noise. A reduction of log spectral distortion (LSD) and increase of signal-to-noise ratio (SNR) of approximately 10 dB and 14 dB are observed.

cs.RO↗

arXiv2016

Robust 3D Localization and Tracking of Sound Sources Using Beamforming and Particle Filtering

In this paper we present a new robust sound source localization and tracking method using an array of eight microphones (US patent pending) . The method uses a steered beamformer based on the reliability-weighted phase transform (RWPHAT) along with a particle filter-based tracking algorithm. The proposed system is able to estimate both the direction and the distance of the sources. In a videoconferencing context, the direction was estimated with an accuracy better than one degree while the distance was accurate within 10% RMS. Tracking of up to three simultaneous moving speakers is demonstrated in a noisy environment.

cs.RO↗

arXiv2016

Localization of Simultaneous Moving Sound Sources for Mobile Robot Using a Frequency-Domain Steered Beamformer Approach

Mobile robots in real-life settings would benefit from being able to localize sound sources. Such a capability can nicely complement vision to help localize a person or an interesting event in the environment, and also to provide enhanced processing for other capabilities such as speech recognition. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a probabilistic post-processor. Results show that a mobile robot can localize in real time multiple moving sources of different types over a range of 5 meters with a response time of 200 ms.

cs.RO↗