Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments

@inproceedings{Feigl2020LocalizationLO,
title={Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments},
author={Tobias Feigl and Andreas Porada and Steve Steiner and Christoffer Loeffler and Christopher Mutschler and Michael Philippsen},
booktitle={VISIGRAPP},
year={2020}
}
• Published in VISIGRAPP 2020
• Computer Science
Augmented Reality (AR) systems are envisioned to soon be used as smart tools across many Industry 4.0 scenarios. The main promise is that such systems will make workers more productive when they can obtain additional situationally coordinated information both seemlessly and hands-free. This paper studies the applicability of today’s popular AR systems (Apple ARKit, Google ARCore, and Microsoft Hololens) in such an industrial context (large area of 1,600m2, long walking distances of 60m between…
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References

SHOWING 1-10 OF 34 REFERENCES

Visual SLAM and Structure from Motion in Dynamic Environments

• Computer Science
ACM Comput. Surv.
• 2018
This article presents for the first time a survey of visual SLAM and SfM techniques that are targeted toward operation in dynamic environments and identifies three main problems: how to perform reconstruction, how to segment and track dynamic objects, and how to achieve joint motion segmentation and reconstruction.

Evaluating and enhancing google tango localization in indoor environments using fiducial markers

• Computer Science
2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
• 2018
A thorough evaluation of the localization accuracy of the Tango platform in different conditions is presented; a fiducial marker-based extension of theTango localization system is presented, which improves the localization estimates in certain conditions and a solution based on the use of additional visual markers is proposed to improve tracking accuracy in dynamic environments where spatial and/or illumination changes may occur.

Monocular Visual-Inertial State Estimation for Mobile Augmented Reality

• Computer Science
2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
• 2017
This work proposes a tightly-coupled, optimization-based, monocular visual-inertial state estimation for robust camera localization in complex indoor and outdoor environments and develops a lightweight loop closure module that is tightly integrated with the state estimator to eliminate drift.

ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

• Computer Science
IEEE Transactions on Robotics
• 2017
ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities, is presented, being in most cases the most accurate SLAM solution.

Parallel Tracking and Mapping for Small AR Workspaces

• Computer Science
2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
• 2007
A system specifically designed to track a hand-held camera in a small AR workspace, processed in parallel threads on a dual-core computer, that produces detailed maps with thousands of landmarks which can be tracked at frame-rate with accuracy and robustness rivalling that of state-of-the-art model-based systems.

Vision meets robotics: The KITTI dataset

• Computer Science
Int. J. Robotics Res.
• 2013
A novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research, using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras and a high-precision GPS/IMU inertial navigation system.

The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

• Computer Science
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 2018
The TUM VI benchmark is proposed, a novel dataset with a diverse set of sequences in different scenes for evaluatingVI odometry, which provides camera images with 1024×1024 resolution at 20 Hz, high dynamic range and photometric calibration, and evaluates state-of-the-art VI odometry approaches on this dataset.

Keyframe-based visual-inertial online SLAM with relocalization

• Computer Science
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 2017
This paper presents a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras based on a real-time capable visual- inertial odometry method that provides locally consistent trajectory and map estimates.

Supervised Learning for Yaw Orientation Estimation

• Computer Science
2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
• 2018
This novel yaw orientation estimation combines a single inertial sensor located at the human's head with inaccurate positional tracking to enable low-cost long-time stable head orientation in NP tracking on 100 $m\times 100$ m.

Detecting position using ARKit II: generating position-time graphs in real-time and further information on limitations of ARKit

• Education
• 2018
ARKit is a framework which allows developers to create augmented reality apps for the iPhone and iPad. In a previous study, we had shown that it could be used to detect position in educational