A-SLAM: Human in-the-loop Augmented SLAM

Sidaoui, Abbas; Zein, Mohammad Kassem; Elhajj, Imad H.; Asmar, Daniel

doi:10.1109/icra.2019.8793539

Cited by 11 publications

(9 citation statements)

References 29 publications

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“…Augmented reality technology has created a new paradigm for human-robot interaction. Through enabling a human-friendly visualization of how a robot is perceiving its environment, an improved human-in-the-loop model can be achieved ( Sidaoui et al, 2019 ; Gong et al, 2017 ). The use of AR technology for robotics has been elevated by the aid of several tools, mainly Vuforia Engine ( Patel et al, 2019 ; Makita et al, 2021 ; Comes et al, 2021 ), RosSharp ( Kästner and Lambrecht, 2019 ; Rosen et al, 2019 ; Qiu et al, 2021 ), ARCore ( Zhang et al, 2019 ; Chacko et al, 2020 ; Mallik and Kapila, 2020 ), and ARKit ( Feigl et al, 2020 ; McHenry et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Bassyouni

Elhajj

2021

Front. Robot. AI

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Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.

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Section: Discussionmentioning

confidence: 99%

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Bassyouni

Elhajj

2021

Front. Robot. AI

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“…This section constitutes the first of two case studies in which JISA is applied. Although the core of this case study is presented in [20,21,22], the works are re-framed (as POC) in the context of JISA. This is done to demonstrate the applicability of JISA in different robotic applications, and to serve as an example of how to apply the framework in a SLAM application.…”

Section: Jisa In Collaborative Slammentioning

confidence: 99%

“…This is done to demonstrate the applicability of JISA in different robotic applications, and to serve as an example of how to apply the framework in a SLAM application. Thus, this section is considered as an evolution and generalization of the approach presented in [20,21,22], where we intend to show how the JISA guideline is followed and the framework is applied, rather than presenting 'JISA in Collaborative SLAM' as a standalone contribution and generating new results.…”

Section: Jisa In Collaborative Slammentioning

confidence: 99%

“…The main contributions of this paper are: 1) Proposing a novel Joint-Initiative Supervised Autonomy (JISA) framework for human-robot interaction, which leverages the robot's SC and the human's SA in order to improve the performance of automated tasks and mitigate the limitations of fully autonomous systems without having a human to completely takeover any task execution. 2) Demonstrating the utility and applicability of JISA by: a) Re-framing the works in [20,21,22] in the context of the proposed JISA framework, with the aim of showing how existing works can be generalized under the JISA framework.…”

Section: Introductionmentioning

confidence: 99%

“…3) Experimental validation and providing preliminary empirical evidence that applying JISA outperforms full autonomy in both POC applications. We note that the experiments in section V were conducted uniquely for this manuscript, while experiments in section IV were introduced in [21,22]. No additional experiments were conducted in section IV, since our goal is to show how a previous approach could be generalized and re-formulated in a more systematic manner under the JISA framework.…”

Section: Introductionmentioning

confidence: 99%

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Human-Robot Interaction via a Joint-Initiative Supervised Autonomy (JISA) Framework

Sidaoui¹,

Daher²,

Asmar³

2021

Preprint

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In this paper, we propose and validate a Joint-Initiative Supervised Autonomy (JISA) framework for Human-Robot Interaction (HRI), in which a robot maintains a measure of its self-confidence (SC) while performing a task, and only prompts the human supervisor for help when its SC drops. At the same time, during task execution, a human supervisor can intervene in the task being performed, based on his/her Situation Awareness (SA). To evaluate the applicability and utility of JISA, it is implemented on two different HRI tasks: grid-based collaborative simultaneous localization and mapping (SLAM) and automated jigsaw puzzle reconstruction. Augmented Reality (AR) (for SLAM) and two-dimensional graphical user interfaces (GUI) (for puzzle reconstruction) are custom-designed to enhance human SA and allow intuitive interaction between the human and the agent. The superiority of the JISA framework is demonstrated in experiments. In SLAM, the superior maps produced by JISA preclude the need for post processing of any SLAM stock maps; furthermore, JISA reduces the required mapping time by approximately 50 percent versus traditional approaches. In automated puzzle reconstruction, the JISA framework outperforms both fully autonomous solutions, as well as those resulting from on-demand human intervention prompted by the agent.

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