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Fast Radar Motion Estimation

Fast Radar Motion Estimation This blog post provides an overview of our paper “Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision” by Roberto Aldera, Daniele De Martini, Matthew Gadd, and Paul Newman which was recently accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 2019. For a quick overview you can take a look at our video: Why radar? Radar is ideal for ego-motion estimation and localisation tasks as it is good at detecting stable environmental features under adverse weather and lighting conditions. An unfiltered radar scan with returns which are not spatially coherent from multiple viewpoints. However, radar measurements are complex for various reasons: The beam is not narrow and tightly focused, Returns are affected by various noise sources, and The interaction of the electromagnetic wave in the environment is more complex than time-of-flight lasers. Challenges Using radar scans for precise ego-motion estimation means we have to deal with complex measurement patterns which are not intuitive. This boils down to a data association problem: “what detail in the last frame is relevant to the detail seen in this frame?” We wanted to make that task simpler. Ideally, we want to operate with returns from artefacts in the scene that are visible from multiple views, excluding from the scan Noise effects Dynamic objects, and Reflections from objects that are highly specular or not visible from multiple views With this design [...]

By |April 10th, 2019|Categories: ORI Blog|Comments Off on Fast Radar Motion Estimation
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