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.
@article{arxiv.1912.08263,
title = {ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization},
author = {Felix Ott and Tobias Feigl and Christoffer Löffler and Christopher Mutschler},
journal= {arXiv preprint arXiv:1912.08263},
year = {2020}
}
Comments
Conf. on Computer Vision and Pattern Recognition (CVPR): Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM 2020