Change search
ReferencesLink to record
Permanent link

Direct link
Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2718-0262
2013 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 164Article in journal (Refereed) Published
Abstract [en]

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.

Place, publisher, year, edition, pages
Springer, 2013. Vol. 164
National Category
Computer Vision and Robotics (Autonomous Systems) Signal Processing
URN: urn:nbn:se:kth:diva-133469DOI: 10.1186/1687-6180-2013-164OAI: diva2:661811

QC 20140114

Available from: 2013-11-05 Created: 2013-11-05 Last updated: 2014-01-14Bibliographically approved
In thesis
1. Infrastructure-free pedestrian localization
Open this publication in new window or tab >>Infrastructure-free pedestrian localization
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Knowledge of your own and other's positions are frequently a prerequisite for acting, leading others, and interacting in and with the environment; to retrieve relevant information and to process and interpret it; and to understand, compile, and learn from observations of the surrounding and its dynamics. This holds for humans as well as for machines and systems made for supporting and controlling them. Consequently, systems which automatically provide position information of peoples are of interest and the larger subject area of this thesis. Position can be obtained from well-known infrastructure based systems such as GPS. However, these systems carry obvious drawbacks in their infrastructure dependence which gives them limited coverage and system robustness. By observing our own ability to localize ourselves, it is obvious that localization without infrastructure at with a better (relative) accuracy is achievable. The development over the last decades of sensor and processing hardware and statistical methods have started to make such localization possible. This thesis specifically concerns systems and statistical methods for infrastructure-free localization. The this primarily deals with statistical methods but also describe hardware in terms of high-level system designs.

For many critical applications such as positioning of emergency responders, dismounted soldiers, and security personnel, it is unsuitable for the positioning system to be dependent on infrastructure or prior knowledge about the environment. Consequently, this thesis deals with systems and methods for infrastructure-free and prior-knowledge-free pedestrian localization. The thesis is specifically concerned with statistical methods but will also cover hardware in terms of high-level system designs. The thesis is composed of an introduction followed by a collection of papers which are divided into two parts, each concerning a separate problem area. The introduction motivates and describes the localization problem in general terms and gives a coherent guide to the articles.

The first group of articles together describes an infrastructure-free system for tactical localization of small units of agents. The physical implementation of the localization system carries the name TOR (Tacitcal lOcatoR) and have been tested on fire fighters during realistic smoke diving exercises. This system primarily depends on pedestrian dead-reckoning based on foot-mounted inertial navigation and inter-agent radio ranging. The core parts of the system which are dealt with are: foot-mounted inertial navigation units which provides dead reckoning of individual agents, system structure and estimation algorithms which, based on the dead reckoning and inter-agent ranging, provides estimates of the agent positions, initialization algorithms for the estimation, and a user interface which exploits voice radio communication and 3D-audio to let the agents hear where they have each other.

The second group of articles concerns low-level processing for extraction of spatial information of camera images (video), a prevailing infrastructure-free data source for relating an agent's position to the environment. These articles are focused on formalization and fast implementations of fundamental processing steps. An implementation of scale-space only relying on integer signal representation of image data and simple arithmetic operations is presented. Further, a unifying theory of feature point orientation assignment is derived and a novel method for the same is presented. Thereafter, the small but frequently occuring processing step in which image gradient samples are binned based on their argument, is treated and three fast solutions with varying properties are suggested. Finally, a localization system based on inertial navigation aided by imagery data is presented.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. viii, 37 p.
National Category
Signal Processing
urn:nbn:se:kth:diva-133443 (URN)978-91-7501-906-2 (ISBN)
Public defence
2013-11-29, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 13:00 (English)

QC 20131105

Available from: 2013-11-05 Created: 2013-11-01 Last updated: 2013-11-05Bibliographically approved

Open Access in DiVA

fulltext(2472 kB)62 downloads
File information
File name FULLTEXT02.pdfFile size 2472 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full textSpringerOpen Journal

Search in DiVA

By author/editor
Nilsson, John-OlofZachariah, DaveSkoog, IsaacHändel, Peter
By organisation
Signal ProcessingACCESS Linnaeus Centre
In the same journal
EURASIP Journal on Advances in Signal Processing
Computer Vision and Robotics (Autonomous Systems)Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 62 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 118 hits
ReferencesLink to record
Permanent link

Direct link