Jan Medvesek, Ph.D.

Despite considerable research efforts over the past decades, indoor localization remains a difficult problem. Although inertial dead reckoning approaches yield accurate results over short periods, their position error typically scales cubically with time. Alternate methods of localization often use radio measurements between mobile and fixed points, either through fingerprinting or range-based methods. However, the accuracy of such approaches is inherently limited by the complex non-linear… Mehr zeigen

Despite considerable research efforts over the past decades, indoor localization remains a difficult problem. Although inertial dead reckoning approaches yield accurate results over short periods, their position error typically scales cubically with time. Alternate methods of localization often use radio measurements between mobile and fixed points, either through fingerprinting or range-based methods. However, the accuracy of such approaches is inherently limited by the complex non-linear effect of clutter and noise on signal propagation. Consequently, substantial training overhead is often required to characterize the spatial evolution of these signal patterns to improve accuracy. This work presents a low-overhead, practical method for characterizing non-linear radio signal propagation for subsequent use in indoor positioning and navigation systems. Specifically, we propose an inertial dead-reckoning based method for building Gaussian process maps that describe the predicted error distributions of a given signal as it varies across space. We further analyze the accuracy of these maps and position estimate techniques based thereon as a function of the inertial training data and the number of known correction points required. Weniger anzeigen

Clutter has a complex effect on radio propagation, and limits the effectiveness of deterministic methods in wireless indoor positioning. In contrast, a Gaussian process (GP) can be used to learn the spatially correlated measurement error directly from training samples, and build a representation from which a position can be inferred. A method of exploiting GP inference to obtain measurement predictions from within a pose graph optimisation framework is presented. However, GP inference has a… Mehr zeigen

Clutter has a complex effect on radio propagation, and limits the effectiveness of deterministic methods in wireless indoor positioning. In contrast, a Gaussian process (GP) can be used to learn the spatially correlated measurement error directly from training samples, and build a representation from which a position can be inferred. A method of exploiting GP inference to obtain measurement predictions from within a pose graph optimisation framework is presented. However, GP inference has a run-time complexity of O(N3) in the number of training samples N, which precludes it from being called in each optimiser iteration. The novel contributions of this work are a method for building an approximate GP inference map and an O(1) bi-cubic interpolation strategy for sampling this map during optimisation. Using inertial, magnetic, signal strength and time-of-flight measurements between four anchors and a single mobile sensor, it is shown empirically that the presented approach leads to decimetre precision indoor pedestrian localisation. Weniger anzeigen

Teaching programming is fast becoming a fundamental learning practice in schools for Science, Technology, Engineering and Maths (STEM) subjects. Through interactive learning with tangible devices such as Lego Mindstorms, students and teachers can explore ways to collect data, analyse and illustrate core principles in STEM subjects. The majority of such educational programmable devices require the use of a PC to program them. This short paper reports on a development project that demonstrates a… Mehr zeigen

Teaching programming is fast becoming a fundamental learning practice in schools for Science, Technology, Engineering and Maths (STEM) subjects. Through interactive learning with tangible devices such as Lego Mindstorms, students and teachers can explore ways to collect data, analyse and illustrate core principles in STEM subjects. The majority of such educational programmable devices require the use of a PC to program them. This short paper reports on a development project that demonstrates a STEM situated and wireless method to enable students to learn pro-gramming. It allows students and teachers to create STEM exper-iments though only a Windows phone with TouchDevelop and a programmable device solution. This focuses their Constructionist learning applied to STEM learning when situated with program-ming capabilities via a Bluetooth based library to operate the UCL Engduino, a customised teaching apparatus based on the Arduino platform. We demonstrate a process for integrating teaching devices with TouchDevelop to expand on pedagogical techniques for both student programmers and educators in programming Weniger anzeigen