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PhD: Learning and recognizing virtual beacons to improve the mobility of visually impaired persons

Do you have a heart for the contribution of artificial intelligence in the construction of physical models, to address the challenges and needs of new mobility services? The inmob joint laboratory on the Nantes campus of the Gustave Eiffel University of France could be the next step in your career.

Context

Research at the Geoloc Laboratory of the Gustave Eiffel University focuses on the development of dynamic positioning methods and systems for travellers. It owns a wide range of skills in the design and programming of beaconless positioning and navigation algorithms with original testing facilities. The Okeenea Digital company is an innovative company from the Okeenea group, specialised for 26 years in the design and supply of accessibility solutions to improve mobility in situations of disability. These two organisations have joined forces in the joint laboratory inmob (disability mapping with INertial signal to facilitate MOBility) to improve the mobility of people with disabilities.

Our ambition is to design new approaches based on artificial intelligence to build physical models that describe the individual specificities of movement of people with disabilities and thus individualise the positioning approaches. This research will increase the performance of current geolocation technologies, which are still insufficient to assist a blind or hearing-impaired person throughout his or her route in complete safety without depending on a very dense network of beacons. To strengthen the labcom team and contribute to the work in artificial intelligence for improved mobility services, we are looking for a research engineer or postdoc, ideally with complementary skills in physics/mathematics and artificial intelligence.

Research topic

Many smartphone-based applications help people navigate. They use GPS, Wi-Fi, Bluetooth, mobile phone data, camera and inertial signals to compute the mobility path consisting of geographic coordinates. But their accuracy is still insufficient for applications where accidents are possible. The estimated routes suffer from drift and positioning errors due to the low quality of the sensors embedded in the smartphone and surrounding obstacles. Beacons (Bluetooth tags, other Internet of Things tags) can be used to improve the performance, but they require a specific infrastructure of beacons deployed in the city. It is also possible to consider virtual points of interest, such as transitions between outside and inside or up/down stairs, to correct the coordinates using map data and the content of the associated geographic information system. Although this concept is well known, it lacks robustness and is not adapted to the specific needs of people with disabilities. Like the applications that integrate the use-case needs (e.g. Soundscape) at the heart of the R&D, this thesis aims to observe the behaviour of visually impaired people to propose new virtual beacons that are specific to them.

Research objectives

This thesis aims at learning the mobility patterns of visually impaired people to build databases of virtual beacons for opportune recalibration of the estimated foot-track in real-time on a smartphone. The work will begin by reading and analysing articles on methods for recognising displacement patterns from signals and images. Existing map matching and pattern recognition codes for pedestrian dead-reckoning algorithms will be redesigned to identify the need for virtual beacon registration. Observing and identifying through experiments the virtual points of interest related to the situation of the disabled and more particularly the visually impaired will be a key stage of the research. It will then be necessary to propose and test automatic learning or deep learning methods to recognise these points of interest. The final objective is to bring these approaches to smartphones to improve and secure the guidance of visually impaired people in multimodal transport.

Required Skills

  • MSc/Eng Degree in signal processing, geomatics, artificial intelligence or computer science
  • Knowledge of signal processing, data mining, applied mathematics, machine learning methods and statistics
  • Knowledge of multi-sensor fusion positioning and estimation theory
  • Strong programming skills in Python, Matlab and TensorFlow (cpp preferred)
  • Experience of working with machine learning applied to physics
  • Proven ability to integrate large-scale data
  • Understanding of connected object technologies and Android programming
  • Ability to effectively integrate research results into publications and intellectual property
  • Creativity and openness to innovation
  • Enthusiasm, responsibility and excellent collaboration skills
  • Passion for producing high quality data
  • Strong oral and written skills in English
Contract TypePhD, 3 years full time (38h30/week) with a start in October 2021 (flexible date)
LocationGEOLOC Laboratory, University Gustave Eiffel, Nantes, France
Deadline31/12/2021 23:50 - Europe/Brussels
ApplicationSend all documents (cover letter, CV, diplomas, list of productions / publications, references) in a single pdf document to valerie.renaudin@univ-eiffel.fr