Visual-Inertial first responder localisation in large-scale indoor training environments.

Simple item page
dc.contributor.authorKhan, Humayun
dc.date.accessioned2022-01-27T20:47:38Z
dc.date.available2022-01-27T20:47:38Z
dc.date.issued2021en
dc.description.abstractAccurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises.en
dc.identifier.urihttps://hdl.handle.net/10092/103332
dc.identifier.urihttp://dx.doi.org/10.26021/12433
dc.languageEnglish
dc.language.isoenen
dc.rightsAll Right Reserveden
dc.rights.urihttps://canterbury.libguides.com/rights/thesesen
dc.titleVisual-Inertial first responder localisation in large-scale indoor training environments.en
dc.typeTheses / Dissertationsen
thesis.degree.disciplineHuman Interface Technologyen
thesis.degree.grantorUniversity of Canterburyen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen
uc.bibnumber3118931
uc.collegeFaculty of Engineeringen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
HumayunPhDThesis_UpdatedCopy.pdf
Size:
11.67 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Engineering: Theses and Dissertations
HIT Lab NZ: Theses and Dissertations