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

Ott F, Feigl T, Löffler C, Mutschler C (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Pages Range: 42-43

Conference Proceedings Title: Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM

Event location: Seattle, WA US

URI: http://openaccess.thecvf.com/content_CVPRW_2020/html/w3/Ott_ViPR_Visual-Odometry-Aided_Pose_Regression_for_6DoF_Camera_Localization_CVPRW_2020_paper.html

DOI: 10.1109/cvprw50498.2020.00029

Open Access Link: http://openaccess.thecvf.com/content_CVPRW_2020/papers/w3/Ott_ViPR_Visual-Odometry-Aided_Pose_Regression_for_6DoF_Camera_Localization_CVPRW_2020_paper.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 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.

Authors with CRIS profile

Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Christoffer Löffler Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE) Ludwig-Maximilians-Universität (LMU) DE Germany (DE)

How to cite

APA:

Ott, F., Feigl, T., Löffler, C., & Mutschler, C. (2020). ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization. In Computer Vision Foundation (CVF) (Eds.), Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM (pp. 42-43). Seattle, WA, US.

MLA:

Ott, Felix, et al. "ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization." Proceedings of the The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA Ed. Computer Vision Foundation (CVF), 2020. 42-43.

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