Efficient Neural Mapping for Localisation of Unmanned Ground Vehicles

Global localisation from visual data is a challenging problem applicable to many robotics domains. Prior works have shown that neural networks can be trained to map images of an environment to absolute camera pose within that environment, learning an implicit neural mapping in the process. In this work we evaluate the applicability of such an approach to real-world robotics scenarios, demonstrating that by constraining the problem to 2-dimensions and significantly increasing the quantity of training data, a compact model capable of real-time inference on embedded platforms can be used to achieve localisation accuracy of several centimetres. We deploy our trained model onboard a UGV platform, demonstrating its effectiveness in a waypoint navigation task. Along with this work we will release a novel localisation dataset comprising simulated and real environments, each with training samples numbering in the tens of thousands.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

UWB FastlyTunable 0.550 GHz RF Transmitter based on Integrated Photonics

Currently, due to the 6G revolution, applications ranging from communication to sensing are experiencing an increasing and urgent need of software-defined ultra-wideband (UWB) and tunable radio frequency (RF) apparatuses with low size, weight, and power consumption (SWaP). Unfortunately, the coexistence of ultra-wideband and software-defined operation, tunability and low SWaP represents a big issue in the current RF technologies. Recently, photonic techniques have been demonstrated to support achieving the desired features when applied in RF UWB transmitters, introducing extremely wide operation and instantaneous bandwidth, tunable filtering, tunable photonics-based microwave mixing with very high port-to-port isolation, and intrinsic immunity to electromagnetic interferences. Moreover, the recent advances in photonics integration also allow to obtain very compact devices. In this article, to the best of our knowledge, the first example of a complete tunable software-defined RF transmitter with low footprint (i.e. on photonic chip) is presented exceeding the state-of-the-art for the extremely large tunability range of 0.5-50 GHz without any parallelization of narrower-band components and with fast tuning (< 200 s). This first implementation represents a breakthrough in microwave photonics

Ultra low-power UWB-RFID system for precise location-aware applications

Ultra low-power radio-frequency identification (RFID) tag with precision localization is often the enabling technology for location-aware sensor applications. Impulse-Radio Ultra-Wideband (IR-UWB) is a promising technology to fulfill the usage requirements in indoor cluttered environment. An ultra low-power precise UWB-RFID localization system is proposed in this paper. The RFID tag is a transmitter comprising of a micro-controller board and a UWB impulse radio board. Power saving and precision localization is achieved by optimization of the circuit design for ultra short pulses as well as system architecture and operation. When 1 s sleep mode is incorporated with 0.72 ms active mode, the tag consumes on average 6.8 uA when pulsing at 3.3 MHz rate with 15.5 dBm peak transmit power. The transmitted pulse is captured by low-cost energy-detection receivers at the locator. Measurement in a 6m×6m typical indoor environment demonstrates that the proposed system is able to achieve positioning accuracy of 10 cm. Due to the high sensitivity of the receiver (-71 dBm), the proposed system can reach a potential reading range of over 100 meters. The ultra low-power consumption, accurate ranging and positioning result, and long reading distance makes the proposed system suitable for a variety of intelligent sensor applications