FN Archimer Export Format
PT J
TI Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization
BT 
AF BOITTIAUX, Clementin
   DUNE-MAILLARD, Claire
   FERRERA, Maxime
   ARNAUBEC, Aurelien
   Marxer, Ricard
   MATABOS, Marjolaine
   VAN AUDENHAEGE, Loic
   Hugel, Vincent
AS 1:1,2,3;2:2;3:1;4:1;5:3;6:4;7:4;8:2;
FF 1:PDG-DFO-SM-PRAO;2:;3:PDG-DFO-SM-PRAO;4:PDG-DFO-SM-PRAO;5:;6:PDG-REM-BEEP-LEP;7:PDG-REM-BEEP-LEP;8:;
C1 Ifremer, Zone Portuaire de Bregaillon, La Seyne-sur-Mer, France
   Universite de Toulon, COSMER, Toulon, France
   Universite de Toulon, Aix Marseille Univ, CNRS, LIS, Toulon, France
   Univ Brest, CNRS, Ifremer, UMR6197 BEEP, F-29280 Plouzane, France
C2 IFREMER, FRANCE
   UNIV TOULON, FRANCE
   UNIV TOULON, FRANCE
   IFREMER, FRANCE
SI TOULON
   BREST
SE PDG-DFO-SM-PRAO
   PDG-REM-BEEP-LEP
UM BEEP-LM2E
IN WOS Ifremer UPR
   WOS Ifremer UMR
   copubli-france
   copubli-univ-france
IF 9.2
TC 4
UR https://archimer.ifremer.fr/doc/00836/94826/102364.pdf
LA English
DT Article
CR MOMARSAT : MONITORING THE MID ATLANTIC RIDGE
DE ;Underwater dataset;long-term visual localization;deep sea;visual localization benchmark;Eiffel Tower vent edifice
AB Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or daynight cycles present a major challenge. Under water, the sources of variability are due to other factors such as water conditions or growth of marine organisms. Yet it remains a major obstacle and a much less studied one, partly due to the lack of data. This paper presents a new deep-sea dataset to benchmark underwater long-term visual localization. The dataset is composed of images from four visits to the same hydrothermal vent edifice over the course of five years. Camera poses and a common geometry of the scene were estimated using navigation data and Structure-from-Motion. This serves as a reference when evaluating visual localization techniques. An analysis of the data provides insights about the major changes observed throughout the years. Furthermore, several well-established visual localization methods are evaluated on the dataset, showing there is still room for improvement in underwater long-term visual localization. The data is made publicly available at seanoe.org/data/00810/92226/. 
PY 2023
PD AUG
SO International Journal Of Robotics Research
SN 0278-3649
PU SAGE Publications
VL 42
IS 9
UT 001002925700001
BP 689
EP 699
DI 10.1177/02783649231177322
ID 94826
ER

EF