Video: https://youtu.be/VhCNOuxNqpA

Abstract: In providing long-range information and significant robustness to environmental conditions radar complements perfectly some of the more commonly used sensing modalities in autonomous driving. However, radar data is also notoriously difficult to work with. Significant, context-dependent sensing artefacts and noise characteristics make interpretation and use of this data a real challenge. In this talk I will describe some of the work done in the Applied AI Lab at Oxford in leveraging learning to enable radar-based perception and navigation. In particular, I will talk about how we use system-level self-supervision - the use of adjacent sensing or subsystems to derive a learning signal during training - in order to make radar data palatable during deployment. I will introduce work that explicitly accounts for the particular noise-characteristics of a radar in order to map from raw radar scans to occupancy grids; I will describe an approach to interpretable ego-motion estimation learning an inherent distraction suppression; and I will give an overview of how we can construct a fully fledged radar-based navigation system.

Bio: Ingmar leads the Applied Artificial Intelligence Lab at Oxford University and is a founding director of the Oxford Robotics Institute. His goal is to enable robots to robustly and effectively operate in complex, real-world environments. His research is guided by a vision to create machines which constantly improve through experience. In doing so Ingmar's work explores a number of intellectual challenges at the heart of robot learning, such as unsupervised scene interpretation, action inference and machine introspection. All the while Ingmar’s research remains grounded in real-world robotics applications such as manipulation, autonomous driving, logistics and space exploration. In 2014 Ingmar co-founded Oxbotica, a multi-award winning provider of mobile autonomy software solutions.