ViPR: Visual-Odometry-Aided Pose Regression for 6DoF Camera Localization

Felix Ott, Tobias Feigl, Christoffer Loffler, Christopher Mutschler; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 42-43

Abstract


Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly from (a series of) images or by merging depth maps with optical flow (OF), research that combines absolute pose regression with OF is limited. We propose ViPR, a novel modular architecture for long-term 6DoF VO that leverages temporal information and synergies between absolute pose estimates (from PoseNet-like modules) and relative pose estimates (from FlowNet-based modules) by combining both through recurrent layers. Experiments on known datasets and on our own Industry dataset show that our modular design outperforms state of the art in long-term navigation tasks.

Related Material


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[bibtex]
@InProceedings{Ott_2020_CVPR_Workshops,
author = {Ott, Felix and Feigl, Tobias and Loffler, Christoffer and Mutschler, Christopher},
title = {ViPR: Visual-Odometry-Aided Pose Regression for 6DoF Camera Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}