Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning

This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a metric space for embedding polar radar scans using CNN and NetVLAD architectures traditionally applied to the visual domain. However, we tailor the feature extraction for more suitability to the polar nature of radar scan formation using cylindrical convolutions, anti-aliasing blurring, and azimuth-wise max-pooling; all in order to bolster the rotational invariance. The enforced metric space is then used to encode a reference trajectory, serving as a map, which is queried for nearest neighbours (NNs) for recognition of places at run-time. We demonstrate the performance of our topological localisation system over the course of many repeat forays using the largest radar-focused mobile autonomy dataset released to date, totalling 280 km of urban driving, a small portion of which we also use to learn the weights of the modified architecture. As this work represents a novel application for FMCW radar, we analyse the utility of the proposed method via a comprehensive set of metrics which provide insight into the efficacy when used in a realistic system, showing improved performance over the root architecture even in the face of random rotational perturbation.Comment: submitted to the 2020 International Conference on Robotics and Automation (ICRA

Design and Implementation of a Low‐Power Wireless Respiration Monitoring Sensor

Wireless devices for monitoring of respiration activities can play a major role in advancing modern home-based health care applications. Existing methods for respiration monitoring require special algorithms and high precision filters to eliminate noise and other motion artifacts. These necessitate additional power consuming circuitry for further signal conditioning. This dissertation is particularly focused on a novel approach of respiration monitoring based on a PVDF-based pyroelectric transducer. Low-power, low-noise, and fully integrated charge amplifiers are designed to serve as the front-end amplifier of the sensor to efficiently convert the charge generated by the transducer into a proportional voltage signal. To transmit the respiration data wirelessly, a lowpower transmitter design is crucial. This energy constraint motivates the exploration of the design of a duty-cycled transmitter, where the radio is designed to be turned off most of the time and turned on only for a short duration of time. Due to its inherent duty-cycled nature, impulse radio ultra-wideband (IR-UWB) transmitter is an ideal candidate for the implementation of a duty-cycled radio. To achieve better energy efficiency and longer battery lifetime a low-power low-complexity OOK (on-off keying) based impulse radio ultra-wideband (IR-UWB) transmitter is designed and implemented using standard CMOS process. Initial simulation and test results exhibit a promising advancement towards the development of an energy-efficient wireless sensor for monitoring of respiration activities