Object-level 3D Semantic Mapping using a Network of Smart Edge Sensors

Autonomous robots that interact with their environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. The scene understanding can further be improved by including object-level information in the map. In this work, we extend a multi-view 3D semantic mapping system consisting of a network of distributed smart edge sensors with object-level information, to enable downstream tasks that need object-level input. Objects are represented in the map via their 3D mesh model or as an object-centric volumetric sub-map that can model arbitrary object geometry when no detailed 3D model is available. We propose a keypoint-based approach to estimate object poses via PnP and refinement via ICP alignment of the 3D object model with the observed point cloud segments. Object instances are tracked to integrate observations over time and to be robust against temporary occlusions. Our method is evaluated on the public Behave dataset where it shows pose estimation accuracy within a few centimeters and in real-world experiments with the sensor network in a challenging lab environment where multiple chairs and a table are tracked through the scene online, in real time even under high occlusions.Comment: 9 pages, 12 figures, 6th IEEE International Conference on Robotic Computing (IRC), Naples, Italy, December 202

Multi-Robot Navigation and Cooperative Mapping in a Circular Topology

Cooperative mapping of an environment by a team of multiple robots is an important problem to advance autonomous robot tasks for example in the field of service robotics or emergency assistance. A precise, global overview of the area the robots are working in, and the ability to navigate this area while avoiding obstacles and collisions between robots is a fundamental requirement for a large number of higher level robot-tasks in those domains. A cooperative mapping, navigation and communication framework supposing unknown initial relative robot positions is developed in this project based on the ROS libraries. It realizes robot displacement, localization and mapping under realistic real-world conditions. Such, the framework provides the underlying functions needed to realize a task of human activity observation in the future. Initially , local maps are individually constructed by the robots using the common gmapping SLAM algorithm from the ROS libraries. The robots are evolving on circles around the scene keeping a constant distance towards it or they can change radius, for example to circumvent obstacles. Local maps are continuously tried to align to compute a joint, global representation of the environment. The hypothesis of a common center point shared between the robots greatly facilitates this task, as the translation between local maps is inherently known and only the rotation has to be found. The map-merging is realized by adapting several methods known in literature to our specific topology. The developed framework is verified and evaluated in real-world scenarios using a team of three robots. Commonly available low-cost robot hardware is utilized. Good performances are reached in multiple scenarios, allowing the robots to construct a global overview by merging their limited local views of the scene

Effectiveness of adjuvant occupational therapy in employees with depression: design of a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Major depressive disorder is among the medical conditions with the highest negative impact on work outcome. However, little is known regarding evidence-based interventions targeting the improvement of work outcomes in depressed employees. In this paper, the design of a randomized controlled trial is presented in order to evaluate the effectiveness of adjuvant occupational therapy in employees with depression. This occupational intervention is based on an earlier intervention, which was designed and proven effective by our research group, and is the only intervention to date that specifically targets work outcome in depressed employees.</p> <p>Methods/Design</p> <p>In a two-arm randomized controlled trial, a total of 117 participants are randomized to either 'care as usual' or <it>' </it>care as usual' with the addition of occupational therapy. Patients included in the study are employees who are absent from work due to depression for at least 25% of their contract hours, and who have a possibility of returning to their own or a new job. The occupational intervention consists of six individual sessions, eight group sessions and a work-place visit over a 16-week period. By increasing exposure to the working environment, and by stimulating communication between employer and employee, the occupational intervention aims to enhance self-efficacy and the acquisition of more adaptive coping strategies. Assessments take place at baseline, and at 6, 12, and 18-month follow-ups. Primary outcome measure is work participation (hours of absenteeism and time until work resumption). Secondary outcome measures are work functioning, symptomatology, health-related quality of life, and neurocognitive functioning. In addition, cost-effectiveness is evaluated from a societal perspective. Finally, mechanisms of change (intermediate outcomes) and potential patient-treatment matching variables are investigated.</p> <p>Discussion</p> <p>This study hopes to provide valuable knowledge regarding an intervention to treat depression, one of the most common and debilitating diseases of our time. If our intervention is proven (cost-) effective, the personal, economic, and health benefits for both patients and employers are far-reaching.</p> <p>Trial registration number</p> <p>NTR2057</p

Factors Associated with Work Participation and Work Functioning in Depressed Workers: A Systematic Review

Background Depression is associated with negative work outcomes such as reduced work participation (WP) (e.g., sick leave duration, work status) and work functioning (WF) (e.g., loss of productivity, work limitations). For the development of evidence-based interventions to improve these work outcomes, factors predicting WP and WF have to be identified. Methods This paper presents a systematic literature review of studies identifying factors associated with WP and WF of currently depressed workers. Results A total of 30 studies were found that addressed factors associated with WP (N = 19) or WF (N = 11). For both outcomes, studies reported most often on the relationship with disorder-related factors, whereas personal factors and work-related factors were less frequently addressed. For WP, the following relationships were supported: strong evidence was found for the association between a long duration of the depressive episode and work disability. Moderate evidence was found for the associations between more severe types of depressive disorder, presence of co-morbid mental or physical disorders, older age, a history of previous sick leave, and work disability. For WF, severe depressive symptoms were associated with work limitations, and clinical improvement was related to work productivity (moderate evidence). Due to the cross-sectional nature of about half of the studies, only few true prospective associations could be identified. Conclusion Our study identifies gaps in knowledge regarding factors predictive of WP and WF in depressed workers and can be used for the design of future research and evidence-based interventions. We recommend undertaking more longitudinal studies to identify modifiable factors predictive of WP and WF, especially work-related and personal factors

Multi-Robot Navigation and Cooperative Mapping in a Circular Topology

Cooperative mapping of an environment by a team of multiple robots is an important problem to advance autonomous robot tasks for example in the field of service robotics or emergency assistance. A precise, global overview of the area the robots are working in, and the ability to navigate this area while avoiding obstacles and collisions between robots is a fundamental requirement for a large number of higher level robot-tasks in those domains. A cooperative mapping, navigation and communication framework supposing unknown initial relative robot positions is developed in this project based on the ROS libraries. It realizes robot displacement, localization and mapping under realistic real-world conditions. Such, the framework provides the underlying functions needed to realize a task of human activity observation in the future. Initially , local maps are individually constructed by the robots using the common gmapping SLAM algorithm from the ROS libraries. The robots are evolving on circles around the scene keeping a constant distance towards it or they can change radius, for example to circumvent obstacles. Local maps are continuously tried to align to compute a joint, global representation of the environment. The hypothesis of a common center point shared between the robots greatly facilitates this task, as the translation between local maps is inherently known and only the rotation has to be found. The map-merging is realized by adapting several methods known in literature to our specific topology. The developed framework is verified and evaluated in real-world scenarios using a team of three robots. Commonly available low-cost robot hardware is utilized. Good performances are reached in multiple scenarios, allowing the robots to construct a global overview by merging their limited local views of the scene

Digital Archive of Memorialization of Mass Atrocities (DAMMA) Workshop Whitepaper

This whitepaper addresses the prospect of creating a digital archive for the memorialization of mass atrocities (abbreviated herein as DAMMA). It is based on the proceedings of a virtual workshop held in October 2021 that addressed questions regarding the scope, form, usages, and development of such an archive

Shall androids dream of genocides? How generative AI can change the future of memorialization of mass atrocities

Abstract The memorialization of mass atrocities such as war crimes and genocides facilitates the remembrance of past suffering, honors those who resisted the perpetrators, and helps prevent the distortion of historical facts. Digital technologies have transformed memorialization practices by enabling less top-down and more creative approaches to remember mass atrocities. At the same time, they may also facilitate the spread of denialism and distortion, attempt to justify past crimes and attack the dignity of victims. The emergence of generative forms of artificial intelligence (AI), which produce textual and visual content, has the potential to revolutionize the field of memorialization even further. AI can identify patterns in training data to create new narratives for representing and interpreting mass atrocities—and do so in a fraction of the time it takes for humans. The use of generative AI in this context raises numerous questions: For example, can the paucity of training data on mass atrocities distort how AI interprets some atrocity-related inquiries? How important is the ability to differentiate between human- and AI-made content concerning mass atrocities? Can AI-made content be used to promote false information concerning atrocities? This article addresses these and other questions by examining the opportunities and risks associated with using generative AIs for memorializing mass atrocities. It also discusses recommendations for AIs integration in memorialization practices to steer the use of these technologies toward a more ethical and sustainable direction