Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization under Challenging Conditions

Venator M, El Himer Y, Aklanoglu S, Bruns E, Maier A (2021)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2021

Journal

IEEE Robotics and Automation Letters IEEE

URI: https://ieeexplore.ieee.org/document/9354898

DOI: 10.1109/LRA.2021.3059571

Abstract

Visual localization provides the basis for many robotics applications such as autonomous navigation or augmented reality. Especially in outdoor scenes, robust localization requires local features which can be reliably extracted and matched under changing conditions. Previous approaches have applied generative image-to-image translation models to align images in a single domain before correspondence search. In this paper, we invert the concept and elaborate why it is more promising to use image domain adaptation for training of robust local features. Integrating this idea into a self-supervised training framework, we show in various experiments covering image matching, visual localization, and scene reconstruction that our Domain-Invariant SuperPoint (DISP) outperforms existing self-supervised methods in terms of repeatability, generalization, and robustness. In contrast to competitive supervised local features, our modular and fully self-supervised approach can be easily adapted to different domains and localization tasks as it does not require ground truth correspondences for training.

Authors with CRIS profile

Moritz Venator Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Audi AG DE Germany (DE)

How to cite

APA:

Venator, M., El Himer, Y., Aklanoglu, S., Bruns, E., & Maier, A. (2021). Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization under Challenging Conditions. IEEE Robotics and Automation Letters. https://dx.doi.org/10.1109/LRA.2021.3059571

MLA:

Venator, Moritz, et al. "Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization under Challenging Conditions." IEEE Robotics and Automation Letters (2021).

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