Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Paper in proceedings, 2018

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applica-tions to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates.

In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches.

Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net

Visual localization

camera pose estimation

long term localization

benchmark

Author

Torsten Sattler

Swiss Federal Institute of Technology in Zürich (ETH)

Will Maddern

University of Oxford

Carl Toft

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Akihiko Torii

Tokyo Institute of Technology

Lars Hammarstrand

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Erik Stenborg

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Daniel Safari

Tokyo Institute of Technology

Technical University of Denmark (DTU)

Masatoshi Okutomi

Tokyo Institute of Technology

Marc Pollefeys

Microsoft Corporation

Swiss Federal Institute of Technology in Zürich (ETH)

Josef Sivic

Czech Technical University in Prague

Institut National de Recherche en Informatique et en Automatique (INRIA)

Fredrik Kahl

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Lund University

Tomas Pajdla

Czech Technical University in Prague

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

10636919 (ISSN)

8601-8610 8578995

Conference on Computer Vision and Pattern Recognition 2018
Salt Lake City, USA,

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA, 2016-01-01 -- 2018-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Robotics

Probability Theory and Statistics

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/CVPR.2018.00897

More information

Latest update

7/9/2019 9