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ViPR: Visual-odometry-aided pose regression for 6DoF camera localization

: Ott, F.; Feigl, T.; Löffler, C.; Mutschler, C.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020. Proceedings : 14-19 June 2020, virtual
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2020
ISBN: 978-1-7281-9360-1
ISBN: 978-1-7281-9361-8
Conference on Computer Vision and Pattern Recognition (CVPR) <2020, Online>
Fraunhofer IIS ()

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 longterm 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 ofthe art in long-term navigation tasks.