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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



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Large-Scale Direct SLAM for Omnidirectional Cameras

We propose a real-time, direct monocular SLAM method for omnidirectional or wide field-of-view fisheye cam- eras. Both tracking (direct image alignment) and mapping (pixel-wise distance filtering) are directly formulated for the unified omnidirectional model, which can model central imaging devices with a field of view well above 150° . This is in stark contrast to existing direct mono-SLAM approaches like DTAM or LSD-SLAM, which operate on rectified images, limiting the field of view to well below 180° . Not only does this allow to observe – and reconstruct – a larger portion of the surrounding environment, but it also makes the system more robust to degenerate (rotation-only) movement. The two main contribution are (1) the formulation of direct image alignment for the unified omnidirectional model, and (2) a fast yet accurate approach to incremental stereo directly on distorted images. We evaluated our framework on real-world sequences taken with a 185◦ fish-eye lens, and compare it to a rectified and a piecewise rectified approach.




Unless stated otherwise, all data in the SLAM for Omnidirectional Cameras Dataset is licensed under a Creative Commons 4.0 Attribution License (CC BY 4.0).

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Conference and Workshop Papers
[]Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (R. Maier, K. Kim, D. Cremers, J. Kautz and M. Niessner), In International Conference on Computer Vision (ICCV), 2017. ([slides] [poster] [dataset] [code]) [bibtex] [pdf]
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French-German Machine Learning Symposium

The French-German Machine Learning Symposium aims to strengthen interactions and inspire collaborations between both countries. We invited some of the leading ML researchers from France and Germany to this two-day symposium to give a glimpse into their research, and engage in discussions on the future of machine learning and how to strengthen research collaborations in ML between France and Germany.

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4Seasons Dataset: We have released a novel dataset for benchmarking multi-weather SLAM in autonomous driving.


Hao Li (Pinscreen) will give a talk in the TUM AI lecture series on April 22nd, 8pm! Livestream