Contact: Lukas von Stumberg, News, Qadeer Khan, Prof. Daniel Cremers

Direct SLAM methods have shown exceptional performance on odometry tasks. However, they are susceptible to dynamic lighting and weather changes while also suffering from a bad initialization on large baselines. To overcome this, we propose GN-Net: a network optimized with the novel Gauss-Newton loss for training weather invariant deep features, tailored for direct image alignment. Our network can be trained with pixel correspondences between images even from different sequences. Experiments on both simulated and real-world datasets demonstrate that our approach is more robust against bad initialization, variations in day-time, and weather changes thereby outperforming state-of-the-art direct and indirect methods. Furthermore, we release an evaluation benchmark for relocalization tracking against different types of weather.

The paper can be downloaded at: https://arxiv.org/abs/1904.11932
The video is available at: https://youtu.be/gcbKeKX2eiE
The supplementary can be downloaded at: gn-net-supplementary.pdf
The relocalization tracking benchmark dataset can be downloaded at: gnnet_benchmark_v1.4.zip
Code related to our benchmark can be found at: https://github.com/Artisense-ai/GN-Net-Benchmark

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Journal Articles
2020
[]		GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization (L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 5, 2020. ([arXiv][video][project page][supplementary]) [bibtex]