Long-Term Visual Localization Revisited
Journal article, 2020

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server.

Cameras

Visual localization

Trajectory

Three-dimensional analysis

benchmark

relocalization

6DOF pose estimation

Robots

Benchmark testing

long-term localization

Solid modelling

Visualization

Author

Carl Toft

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Erik Stenborg

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Lars Hammarstrand

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Will Maddern

Nuro

Marc Pollefeys

Swiss Federal Institute of Technology in Zürich (ETH)

Akihiko Torii

Tokyo Institute of Technology

Daniel Safari

Masatoshi Okutomi

Tokyo Institute of Technology

Josef Sivic

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

Tomas Pajdla

Czech Technical University in Prague

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Fredrik Kahl

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence

0162-8828 (ISSN)

Vol. N/A N/A N/A-N/A N/A

Integrering av geometri och semantik i datorseende

Swedish Research Council (VR), 2017-01-01 -- 2020-12-31.

Semantic Mapping and Visual Navigation for Smart Robots

Swedish Foundation for Strategic Research (SSF), 2016-05-01 -- 2021-06-30.

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories

Signal Processing

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/TPAMI.2020.3032010

More information

Created

12/15/2020