Print Email Facebook Twitter Insect-Inspired Visual Guidance Title Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation? Author Verheyen, Jan (TU Delft Aerospace Engineering) Contributor de Croon, G.C.H.E. (mentor) Dupeyroux, J.J.G. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering | Control & Simulation Date 2022-07-12 Abstract Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form. Subject Long-range navigationNeuromorphic systemsEvent- based CameraRGB CameraGPSGNSS To reference this document use: http://resolver.tudelft.nl/uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca Part of collection Student theses Document type master thesis Rights © 2022 Jan Verheyen Files PDF MasterThesis_JanVerheyen_ ... 458192.pdf 23.93 MB Close viewer /islandora/object/uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca/datastream/OBJ/view