Trilateration Using Motion Models

Martin Larsson; Erik Tegler; Kalle Astrom; Magnus Oskarsson

doi:10.23919/FUSION49751.2022.9841240

Trilateration Using Motion Models

Martin Larsson, Erik Tegler, Kalle Astrom, Magnus Oskarsson

Combain Mobile AB

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results.

Original language	English
Title of host publication	2022 25th International Conference on Information Fusion, FUSION 2022
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	01-07
ISBN (Electronic)	9781737749721
DOIs	https://doi.org/10.23919/FUSION49751.2022.9841240
Publication status	Published - 2022
Event	25th International Conference on Information Fusion, FUSION 2022 - Linkoping, Sweden Duration: 2022 Jul 4 → 2022 Jul 7

Conference

Conference	25th International Conference on Information Fusion, FUSION 2022
Country/Territory	Sweden
City	Linkoping
Period	2022/07/04 → 2022/07/07

Subject classification (UKÄ)

Computer Vision and Robotics (Autonomous Systems)

Keywords

IMU
minimal solvers
motion model
odometry
RANSAC
time-of-arrival
Trilateration

Access to Document

10.23919/FUSION49751.2022.9841240

1 Doctoral Thesis (compilation)

Localization using Distance Geometry: Minimal Solvers and Robust Methods for Sensor Network Self-Calibration
Larsson, M., 2022, Lund: Mathematics Centre for Mathematical Sciences Lund University Lund. 200 p.
Research output: Thesis › Doctoral Thesis (compilation)

Open Access
File

Cite this

@inproceedings{b568018935754df0847f6af87ee9e066,

title = "Trilateration Using Motion Models",

abstract = "In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results. ",

keywords = "IMU, minimal solvers, motion model, odometry, RANSAC, time-of-arrival, Trilateration",

author = "Martin Larsson and Erik Tegler and Kalle Astrom and Magnus Oskarsson",

note = "Publisher Copyright: {\textcopyright} 2022 International Society of Information Fusion.; 25th International Conference on Information Fusion, FUSION 2022 ; Conference date: 04-07-2022 Through 07-07-2022",

year = "2022",

doi = "10.23919/FUSION49751.2022.9841240",

language = "English",

pages = "01--07",

booktitle = "2022 25th International Conference on Information Fusion, FUSION 2022",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

Larsson, M , Tegler, E , Astrom, K & Oskarsson, M 2022, Trilateration Using Motion Models. in 2022 25th International Conference on Information Fusion, FUSION 2022. IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 01-07, 25th International Conference on Information Fusion, FUSION 2022, Linkoping, Sweden, 2022/07/04. https://doi.org/10.23919/FUSION49751.2022.9841240

TY - GEN

T1 - Trilateration Using Motion Models

AU - Larsson, Martin

AU - Tegler, Erik

AU - Astrom, Kalle

AU - Oskarsson, Magnus

PY - 2022

Y1 - 2022

N2 - In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results.

AB - In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same type of registration framework, by only changing the transformation group. The registration is based on a test and hypothesis framework, such as RANSAC, and we present novel and fast minimal solvers that can be used to bootstrap such methods. The system is tested on both synthetic and real data with promising results.

KW - IMU

KW - minimal solvers

KW - motion model

KW - odometry

KW - RANSAC

KW - time-of-arrival

KW - Trilateration

U2 - 10.23919/FUSION49751.2022.9841240

DO - 10.23919/FUSION49751.2022.9841240

M3 - Paper in conference proceeding

AN - SCOPUS:85136555411

SP - 1

EP - 7

BT - 2022 25th International Conference on Information Fusion, FUSION 2022

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th International Conference on Information Fusion, FUSION 2022

Y2 - 4 July 2022 through 7 July 2022

ER -

Trilateration Using Motion Models

Abstract

Conference

Subject classification (UKÄ)

Keywords

Access to Document

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Research output

Localization using Distance Geometry: Minimal Solvers and Robust Methods for Sensor Network Self-Calibration

Cite this