Long-Term Visual Localization Revisited
Artikel i vetenskaplig tidskrift, 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

Författare

Carl Toft

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Erik Stenborg

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Lars Hammarstrand

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Will Maddern

Nuro

Marc Pollefeys

Eidgenössische Technische Hochschule 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

Ceske Vysoke Uceni Technicke v Praze

Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Fredrik Kahl

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

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

Vetenskapsrådet (VR), 2017-01-01 -- 2020-12-31.

Semantisk kartering & visuell navigering för smarta robotar

Stiftelsen för Strategisk forskning (SSF), 2016-05-01 -- 2021-06-30.

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Ämneskategorier

Signalbehandling

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

DOI

10.1109/TPAMI.2020.3032010

Mer information

Skapat

2020-12-15