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Home Research Areas Visual SLAM GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization

GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization

Abstract

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.

Downloads

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_v1.0.zip

Results Oxford Robotcar

The following new results include comparisons to D2-Net and SuperPoint. These keypoint-based methods were designed to be used in combination with the PnP algorithm in a RANSAC scheme. We also show results for a GN-Net model which was only trained on the synthetic CARLA benchmark, but tested on the Oxford sequences (dashed green).

Results Sunny-Overcast

Results Sunny-Rainy

Results Sunny-Snowy

Results Overcast-Rainy

Results Overcast-Snowy

Results Rainy-Snowy


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Preprints
2019
[]GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization (L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers), In preprint, 2019. ([arxiv][video][project page][supplementary]) [bibtex]
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