Finding Objects Faster in Dense Environments Using a Projection Augmented Robotic Arm

Gacem, Hind; Bailly, Gilles; Eagan, James; Lecolinet, Éric

doi:10.1007/978-3-319-22698-9_15

Cited by 5 publications

(13 citation statements)

References 28 publications

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“…Gacem et al [64] introduced an AR-integrated system, "Projection-Augmented Arm (PAA)", that incorporates projection technology with a motorized robotic arm to assist human user during the processes of locating and finding objects in a dense environment. In [65], an infrared camera was integrated to a mobile robot to enhance autonomous robot navigation via utilizing projection SAR technology.…”

Section: Ar In Robot Control and Planningmentioning

confidence: 99%

Augmented Reality for Robotics: A Review

2020

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Augmented reality (AR) is used to enhance the perception of the real world by integrating virtual objects to an image sequence acquired from various camera technologies. Numerous AR applications in robotics have been developed in recent years. The aim of this paper is to provide an overview of AR research in robotics during the five year period from 2015 to 2019. We classified these works in terms of application areas into four categories: (1) Medical robotics: Robot-Assisted surgery (RAS), prosthetics, rehabilitation, and training systems; (2) Motion planning and control: trajectory generation, robot programming, simulation, and manipulation; (3) Human-robot interaction (HRI): teleoperation, collaborative interfaces, wearable robots, haptic interfaces, brain-computer interfaces (BCIs), and gaming; (4) Multi-agent systems: use of visual feedback to remotely control drones, robot swarms, and robots with shared workspace. Recent developments in AR technology are discussed followed by the challenges met in AR due to issues of camera localization, environment mapping, and registration. We explore AR applications in terms of how AR was integrated and which improvements it introduced to corresponding fields of robotics. In addition, we summarize the major limitations of the presented applications in each category. Finally, we conclude our review with future directions of AR research in robotics. The survey covers over 100 research works published over the last five years.

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Section: Ar In Robot Control and Planningmentioning

confidence: 99%

Augmented Reality for Robotics: A Review

2020

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“…Most of the research focuses on the issues of input [39,42,49,63], technical infrastructure and display technology [27,28,43,60], and the models of collaboration and display space management [13,41]. Despite being an obvious challenge in these spaces, guiding attention beyond the immediate field of view has not been studied closely for this kind of environment (however, see [7,17]). Instead there is relevant related work in virtual environments [6,11,54,57], AR [19,20,26,35,53], outdoor and public spaces [36,47], 360 • videos [32,33,51,52], and mobile or mid-sized displays, where the first techniques to point to off-screen locations (a different problem for which similar solutions apply) were invented [5,21,22,38,64].…”

Section: Related Workmentioning

confidence: 99%

“…One particular problem that arises in FCDs is that when objects or windows need the user's attention, it can be difficult to notify the user and draw their gaze to a particular location in the room [7,17]. A main cause is that many of the digital artefacts in an FCD may be behind or above the user, and out of their field of view -a constraint that does not exist for monitors or even large displays [48].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Notification Techniques for Out-of-View Objects in Full-Coverage Displays

Petford

Carson

Nacenta

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Full-coverage displays can place visual content anywhere on the interior surfaces of a room (e.g., a weather display near the coat stand). In these settings, digital artefacts can be located behind the user and out of their field of viewmeaning that it can be difficult to notify the user when these artefacts need attention. Although much research has been carried out on notification, little is known about how best to direct people to the necessary location in room environments. We designed five diverse attention-guiding techniques for full-coverage display rooms, and evaluated them in a study where participants completed search tasks guided by the different techniques. Our study provides new results about notification in full-coverage displays: we showed benefits of persistent visualisations that could be followed all the way to the target and that indicate distance-to-target. Our findings provide useful information for improving the usability of interactive full-coverage environments. CCS CONCEPTS • Human-centered computing → Interaction techniques; Empirical studies in HCI; Empirical studies in ubiquitous and mobile computing.

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“…Kim et al [18] presented an algorithm for Keystone correction using a single camera that computes the homography from a pattern, determining a scaled rigid body transform. Gacem et al [19] developed system aimed at locating objects inside warehouse-like environments using a robotic arm equipped with a pico projector and 8 IR cameras. The distortion is corrected by estimating the corners of the projection surface and then calculating the homography between biggest rectangle inside the projection area and the original image.…”

Section: Image Correctionmentioning

confidence: 99%

“…We use a similar approach to [19] to correct the image, although our system uses a single camera. Because of this, we can only find those rectangles that fall inside the region of the projection surface captured by the camera.…”

Section: Image Correctionmentioning

confidence: 99%

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

et al. 2017

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Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.

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Finding Objects Faster in Dense Environments Using a Projection Augmented Robotic Arm

Cited by 5 publications

References 28 publications

Augmented Reality for Robotics: A Review

Augmented Reality for Robotics: A Review

A Comparison of Notification Techniques for Out-of-View Objects in Full-Coverage Displays

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

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