Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF

Atanas Mirchev; Baris Kayalibay; Patrick van der Smagt; Justin Bayer

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arXiv · 2006.10178

Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF

Atanas Mirchev·Baris Kayalibay·Patrick van der Smagt·Justin Bayer

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Abstract

We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages both learning and domain knowledge from multiple-view geometry and rigid-body dynamics. This results in an expressive predictive model of the world, often missing in current state-of-the-art visual SLAM solutions. The combination of variational inference, neural networks and a differentiable raycaster ensures that our model is amenable to end-to-end gradient-based optimisation. We evaluate our approach on realistic unmanned aerial vehicle flight data, nearing the performance of state-of-the-art visual-inertial odometry systems. We demonstrate the applicability of the model to generative prediction and planning.

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BibTeX RIS

Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer. 2020-06-17. Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF. https://arxiv.org/abs/2006.10178

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