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Visual-Odometry-aided Pose Regression for 6DoF Camera Localization

Ott F, Feigl T, Löffler C, Mutschler C (2019)

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Publication Language: English

Publication Type: Other publication type

Publication year: 2019

Publisher: arXiv

Journal Issue: arXiv:1912.08263

URI: https://arxiv.org/pdf/1912.08263.pdf

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 the optical flow (OF), research that combines absolute pose regression with OF is limited.
We propose ViPR, a novel architecture for long-term 6DoF VO that leverages synergies between absolute pose estimates (from PoseNet-like architectures) and relative pose estimates (from FlowNet-based architectures) by combining both through recurrent layers. Experiments with known publicly available datasets and with our own Industry dataset show that our novel design outperforms existing techniques in long-term navigation tasks.

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How to cite

APA:

Ott, F., Feigl, T., Löffler, C., & Mutschler, C. (2019). Visual-Odometry-aided Pose Regression for 6DoF Camera Localization. arXiv.

MLA:

Ott, Felix, et al. Visual-Odometry-aided Pose Regression for 6DoF Camera Localization. arXiv, 2019.

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