INTRODUCTION:Visiting a patient's living environment is important for occupational therapists, albeit costly and time consuming. MapIt is a mobile app producing a 3D representation of a home with the possibility of taking measurements. The purpose of this study was to explore the utility of a 3D representation of a patient's home for the clinical practice of occupational therapists. METHODS:Case study in which the unit of analysis was the utility of MapIt as defined by ISO 9241-11:2018 and as perceived by occupational therapists in four different occupational therapy clinical settings (Canada). Onsite observations with 10 occupational therapists (and their patients) were triangulated with data from interviews, diaries, and logbooks. Inductive thematic condensation led to emerging conclusions for each clinical setting, fuelling the next case data collection and analysis. Inter-case analysis was corroborated by additional occupational therapists, through crowdsourcing and expert review. RESULTS:Occupational therapists' clinical reasoning was supported by the MapIt app, enhancing and streamlining their work and inducing adjustments to treatment plans. Occupational therapists saw and measured the patient's environment remotely, to better match person-environment-occupation and promote occupational engagement. MapIt's 3D representations were judged useful to communicate between occupational therapists and stakeholders, to educate, allow continuity, optimise resources, minimise the patient's time on a waitlist for homecare, and save time for everyone. DISCUSSION:MapIt allowed occupational therapists who performed home visits to bring a little of the patients' home to their office, whereas occupational therapists without access to the home could see it and take measurements. MapIt's utility was confirmed for practice in clinical settings and for better continuity of care between settings. CONCLUSION:MapIt makes it possible for occupational therapists to 'walk around' the patient's home remotely, but the possibility of measuring environmental elements is a 3D model's true added value over currently used photos or short videos.

Underground stope mapping is crucial to evaluate the quantity of blasted rock and the site integrity. In recent years, lidar-equipped drones have been used to map stopes with higher precision and without blind spots. However, they have limitations, such as large size, challenging lidar positioning on the drone, limited flight time for detailed visual inspections, and unreliable communication underground. This paper discusses the development of a compact tethered drone called the NetherDrone, specifically designed for stope inspections. The NetherDrone uses custom ducted propulsion to increase thrust efficiency by 50%. It reduces the propellers' diameter and overall frame while maintaining an adequate lifting capability with low power consumption. The drone features an onboard 120 m tether spool for communication and power transmission, as well as a rotating arm to deploy the cable and reduce yaw moments from the tether tension. Flights in a real stope demonstrated that the drone could effectively move at least 50 m deep into a complex stope, complete a detailed lidar scan, visually scan one face of the stope in close proximity during 20 min, travel a total distance of 270 m, and maintain communications with an operator at all times through the tether.

During the pandemic, the MapIt app was integrated into an occupational therapy program to support remote learning of built environment adaptation. MapIt maps rooms in a home, then generates a 3D model for viewing and taking virtual measurements. The students express that the use of MapIt during their training leads to embodying the roles expected of an occupational therapist. To inspire other good pedagogical ideas, this article presents how MapIt can support learning in authentic situations, a key element of a professionalization path, approaching the realities experienced by patients, clients, or caregivers.

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.

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 min intervals during sunset using a Google Tango phone in a real apartment.

Énoncé des implications de la recherche Durant la pandémie, l’application MapIt a été intégrée dans un programme d’ergothérapie pour soutenir l’apprentissage à distance de l’adaptation de l’environnement bâti. MapIt permet de cartographier des pièces d’un domicile, puis d’en générer un modèle en 3D pour la visualisation et la prise de mesures virtuelles. Les étudiantes expriment que le recours à MapIt durant leur formation mène à incarner les rôles attendus d’une ergothérapeute. Pour inspirer d’autres bonnes idées pédagogiques, cet article présente comment MapIt peut soutenir l’apprentissage en situations authentiques, un élément clé d’un parcours de professionnalisation, s’approchant des réalités vécues par les personnes patientes, clientes ou intervenantes

This work presents an extension of graph-based SLAM methods to exploit the potential of 3D laser scans for loop detection. Every high-dimensional point cloud is replaced by a compact global descriptor, whereby a trained detector decides whether a loop exists. Searching for loops is performed locally in a variable space to consider the odometry drift. Since closing a wrong loop has fatal consequences, an extensive verification is performed before acceptance. The proposed algorithm is implemented as an extension of the widely used state-of-the-art library RTAB-Map, and several experiments show the improvement: During SLAM with a mobile service robot in changing indoor and outdoor campus environments, our approach improves RTAB-Map regarding total number of closed loops. Especially in the presence of significant environmental changes, which typically lead to failure, localization becomes possible by our extension. Experiments with a car in traffic (KITTI benchmark) show the general applicability of our approach. These results are comparable to the state-of-the-art LiDAR method LOAM. The developed ROS package is freely available.

In health care, a telepresence robot could be used to have a clinician or a caregiver assist seniors in their homes, without having to travel to these locations. However, the usability of these platforms for such applications requires that they can navigate and interact with a certain level of autonomy. For instance, robots should be able to go to their charging station in case of low energy level or telecommunication failure. The remote operator could be assisted by the robot’s capabilities to navigate safely at home and to follow and track people with whom to interact. This requires the integration of autonomous decision-making capabilities on a platform equipped with appropriate sensing and action modalities, which are validated out in the laboratory and in real homes. To document and study these translational issues, this article presents such integration on a Beam telepresence platform using three open-source libraries for integrated robot control architecture, autonomous navigation and sound processing, developed with real-time, limited processing and robustness requirements, so that they can work in real-life settings. Validation of the resulting platform, named SAM, is presented based on the trials carried out in 10 homes. Observations made provide guidance on what to improve and will help identify interaction scenarios for the upcoming usability studies with seniors, clinicians and caregivers.

Background Home adaptation processes enhancing occupational engagement rely on identifying environmental barriers, generally during time-consuming home visits performed by occupational therapists (OTs). Relevance of a 3D model to the OT’s work has been attested, but a convenient and consumer-available technology to map the home environment in 3D is currently lacking. For instance, such a technology would support the exploration of home adaptations for a person with disability, with or without an OT visit. Objective The aim of this study was to document the development and acceptability of a 3D mapping eHealth technology, optimizing its contribution to the OT’s work when conducting assessments in which home representations are essential to fit a person’s needs. Methods A user-centered perspective, embedded in a participatory design framework where users are considered as research partners (not as just study participants), is reported. OTs, engineers, clinicians, researchers, and students, as well as the relatives of older adults contributed by providing ongoing feedback (eg, demonstrations, brainstorming, usability testing, questionnaires, prototyping). System acceptability, as per the Nielsen model, is documented by deductively integrating the data. Results A total of 24 stakeholders contributed significantly to MapIt technology’s co-design over a span of 4 years. Fueled by the objective to enhance MapIt’s acceptability, 11 iterations lead to a mobile app to scan a room and produce its 3D model in less than 5 minutes. The app is available for smartphones and paired with computer software. Scanning, visualization, and automatic measurements are done on a smartphone equipped with a motion sensor and a camera with depth perception, and the computer software facilitates visualization, while allowing custom measurement of architectural elements directly on the 3D model. Stakeholders’ perception was favorable regarding MapIt’s acceptability, testifying to its usefulness (ie, usability and utility). Residual usability issues as well as concerns about accessibility and scan rendering still need to be addressed to foster its integration to a clinical context. Conclusions MapIt allows to scan a room quickly and simply, providing a 3D model from images taken in real-world settings and to remotely but jointly explore home adaptations to enhance a person’s occupational engagement.

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.

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.

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.

One of the greatest demographic and social transformations facing developed countries is caused by the aging of their populations, increased life expectancy and related challenges. Incidentally, this supports the fast growing development of technology used to provide home care, including robotics. In particular, mobile telepresence robotic platforms are now commercially available and provide mobility to sensors, actuators and interactive devices into real world settings, without having to engineer the environment for their use. However, usability of these platforms for such applications requires that they be equipped with some autonomy for navigation and interaction. This paper presents three open source libraries developed to address the challenges of navigation, artificial audition and integration that we have been developing for the design of a home assistance robot. These libraries are being developed with realtime, limited processing and robustness requirements in mind so that they work out of the lab and into real homes. The current usage of these libraries is illustrated using SAM, an enhanced Beam+ platform.

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.

The aging population is putting increasing pressure on health care systems in many developed countries, and maintaining quality of care while controlling costs becomes a major issue that needs to be addressed. With platforms now available at 2,000 $ US, telepresence robots are one potential solution to provide remote care services to elders living in their homes. However, they need improved capabilities to make them more than simple "Skype on wheels" devices. To make telepresence robots suitable for remote home care applications, they must offer enhanced and robust functionalities such as autonomous navigation, artificial audition and vital sign monitoring. Therefore, we integrated such capabilities on a beam+ platform using a robot control architecture, demonstrating the feasibility of adding these capabilities on a commercial robot.

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.

A telepresence mobile robot is a remote-controlled, wheeled device with wireless internet connectivity for bidirectional audio, video and data transmission. In health care, a telepresence robot could be used to have a clinician or a caregiver assist seniors in their homes without having to travel to these locations. Many mobile telepresence robotic platforms have recently been introduced on the market, bringing mobility to telecommunication and vital sign monitoring at reasonable costs. What is missing for making them effective remote telepresence systems for home care assistance are capabilities specifically needed to assist the remote operator in controlling the robot and perceiving the environment through the robot's sensors or, in other words, minimizing cognitive load and maximizing situation awareness. This paper describes our approach adding navigation, artificial audition and vital sign monitoring capabilities to a commercially available telepresence mobile robot. This requires the use of a robot control architecture to integrate the autonomous and teleoperation capabilities of the platform.

Commercial telepresence robots provide video, audio, and proximity data to remote operators through a teleoperation user interface running on standard computing devices. As new modalities such as force sensing and sound localization are being developed and tested on advanced robotic platforms, ways to integrate such information on a teleoperation interface are required. This paper demonstrates the use of visual representations of forces and sound localization in a 3D teleoperation interface. Forces are represented using colors, size, bar graphs and arrows, while speech or ring bubbles are used to represents sound positions and types. Validation of these modalities is done with 31 participants using IRL-1/TR, a humanoid platform equipped with differential elastic actuators to provide compliance and force control of its arms and capable of sound source localization. Results suggest that visual representations of interaction force and sound source can provide appropriately useful information to remote operators.

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.