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Ludvig Ericson

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About me

Though robotics and artificial intelligence has come a long way over the years, a lot of work remains until the uprising can finally begin. The first order of business is actual spatial understanding and reasoning.

Spatial understanding is a key capability in autonomous robotic systems. Everyday constructions such as walls, floors, and ceilings still by and large elude robotic understanding. My work seeks to remedy this by using so-called spatial priors, in the context of common robotics problems, such as exploration, search-and-rescue, and in the longer term, social robotics. These spatial priors are typically encoded as deep generative models, such as GANs or VAEs.


J. Tang et al., "GCNv2 : Efficient Correspondence Prediction for Real-Time SLAM," IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3505-3512, 2019.

Conference papers

M. Welle et al., "On the use of Unmanned Aerial Vehicles for Autonomous Object Modeling," in 2017 European Conference on Mobile Robots, ECMR 2017, 2017.


Introduction to Robotics (DD2410), assistant | Course web

Machine Learning (DD2421), assistant | Course web