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Privacy preserving localization and mapping from uncalibrated cameras

Geppert, Marcel ; Larsson, Viktor LU ; Speciale, Pablo ; Schonberger, Johannes L and Pollefeys, Marc (2021) 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 p.1809-1819
Abstract
Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the... (More)
Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the privacy of the image contents. Furthermore, we also derive a solution to bootstrapping maps from scratch using only uncalibrated devices. Our approach provides comparable performance to the calibrated scenario and the privacy compromising alternatives based on traditional point features. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
conference location
Virtual, Online, United States
conference dates
2021-06-19 - 2021-06-25
external identifiers
  • scopus:85123195257
DOI
10.1109/CVPR46437.2021.00185
language
English
LU publication?
no
id
45dfab78-623c-45b8-a9ad-b213f3244adc
date added to LUP
2022-09-06 13:21:33
date last changed
2022-09-20 18:58:16
@inproceedings{45dfab78-623c-45b8-a9ad-b213f3244adc,
  abstract     = {{Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the privacy of the image contents. Furthermore, we also derive a solution to bootstrapping maps from scratch using only uncalibrated devices. Our approach provides comparable performance to the calibrated scenario and the privacy compromising alternatives based on traditional point features.}},
  author       = {{Geppert, Marcel and Larsson, Viktor and Speciale, Pablo and Schonberger, Johannes L and Pollefeys, Marc}},
  booktitle    = {{2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  language     = {{eng}},
  pages        = {{1809--1819}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Privacy preserving localization and mapping from uncalibrated cameras}},
  url          = {{http://dx.doi.org/10.1109/CVPR46437.2021.00185}},
  doi          = {{10.1109/CVPR46437.2021.00185}},
  year         = {{2021}},
}