April 2014: We received the Best Paper Award at IPSN 2014 for the paper “Lightweight map matching for indoor localisation using conditional random fields”!
at EWSN 2014 for the poster “A Case for Magneto-Inductive Indoor Localization”!
AIMS) – directed by S. Roberts and N. Trigoni. It combines four themes: Robotics, Machine Learning, Verification&Control and Sensor&Actuator Networks. Several funded PhD positions are available every year with the first intake in 2014/2015 and the last in 2018/2019.
Research into sensor networks, led by Professor Niki Trigoni and Dr Andrew Markham, focuses on localization, communication and coordination algorithms for networked sensors. Here is a list of current and recent research projects funded by EPSRC, EOARD and other industry / government sources. For more details, please see the group’s publications.
Agile Asset Monitoring for Construction Sites (supported by Laing O’Rourke). This project involves outfitting a test construction site with a variety of sensors, including active/passive RFIDs, inertial sensors, motion detectors, magnetic sensors and GPS receivers. These sensors, which have different accuracy and energy profiles, will be connected through an ad hoc wireless network, and will be dynamically activated to take into account network conditions and tracking accuracy requirements. The focus will be on developing robust tracking techniques for challenging environments, e.g. locating objects among clutter, immersed in water or even buried underground. The project will also explore how to further improve tracking accuracy by exploiting existing CCTV infrastructure. For more details, please see the project’s website.
Intelligent Workspace Acquisition, Comprehension and Exploitation for Mobile Autonomy in Infrastructure Denied Environments (in collab. with the Robotics Research Group – supported by EPSRC). The aim of this project is to develop robust navigation and communications infrastructure for mobile agents (robots) to be able to interpret and navigate unstructured, unmodified workspaces, alone or in concert with other agents. Ground vehicles can significantly speed up the process of creating workspace representations by sharing the sensor data that they acquire, instead of relying only on their onboard sensors. In GPS-denied environments, the goal is to investigate how to exploit a variety of opportunistic signals (e.g. WiFi / BTLE, TV) as well as environment signatures (e.g. magnetic distortions due to metallic objects) to weave together vehicles’ experiences even if they have been acquired using different sensor modalities. This will allow for building multi-modal maps and pave the way for long term navigation in infrastructure poor workspaces.
Lightweight and Robust Indoor Positioning (supported by CSC). The aim of this project is to develop a positioning system that works reliably across users, motion modes and device types, is flexible to make the most of available resources (sensor data and maps), and is capable of learning and improving over time. The envisaged system will be able to work flexibly with or without bespoke positioning infrastructure. It should be lightweight enough to run on the user’s mobile device without having to offload the position computation to the cloud. The basis of the positioning system is a robust pedestrian dead reckoning algorithm that works accurately for different motion modes (texting, hand swinging, device in trouser / shirt pocket, and so on) despite the noisy nature of inertial measurement units embedded in smart devices. In the presence of environment maps (e.g. floorplans, radio or magnetic fingerprint maps), the positioning system should be able to exploit them to correct the drift of the inertial trajectory, while improving the quality of the available maps as it acquires more data. Finally, the positioning system should be able to learn and improve its accuracy the more it is employed by a user in a particular setting. For more details, please see the project’s publications.
Being There: Humans and Robots in Public Spaces (supported by EPSRC). This is a cross-disciplinary project that brings together five teams of researchers, from Oxford, Exeter, Bath, Queen Mary and Bristol. The team will explore how one might preserve privacy, encourage trust in human-robot communication, and the implications of using a robot to stand in for the physical presence of a human. Our team’s focus is on accurately capturing the location and orientation of individuals and robiots using low frequency magnetic fields. This technology has already been shown to yield accuracy in the context of underground animal tracking. However, the sensors used in that setting are too large and impractical to deploy in an indoor environment. In this project, we will design and use small magnetic beacons that generate low frequency fields. These fields will then be sensed by low-power miniature receivers carried by individuals and robots, thus enabling them to position and orient themselves relative to beacons. The most important property of MI positioning is that obstacles like walls, floors and people that heavily impact the performance of competing approaches (e.g. WiFi-based positioning) are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation, including the presence of ferrous materials and sensitivity to user rotation. We aim to overcome these challenges, through signal processing and sensor fusion across multiple system layers. For more details, please see the project’s publications.