TY - JOUR
T1 - Underwater AUV Navigation Dataset in Natural Scenarios
AU - Wang, Can
AU - Cheng, Chensheng
AU - Yang, Dianyu
AU - Pan, Guang
AU - Zhang, Feihu
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/9
Y1 - 2023/9
N2 - Autonomous underwater vehicles (AUVs) are extensively utilized in various autonomous underwater missions, encompassing ocean environment monitoring, underwater searching, and geological exploration. Owing to their profound underwater capabilities and robust autonomy, AUVs have emerged as indispensable instruments. Nevertheless, AUVs encounter several constraints in the domain of underwater navigation, primarily stemming from the cost-intensive nature of inertial navigation devices and Doppler velocity logs, which impede the acquisition of navigation data. Underwater simultaneous localization and mapping (SLAM) techniques, along with other navigation approaches reliant on perceptual sensors like vision and sonar, are employed to augment the precision of self-positioning. Particularly within the realm of machine learning, the utilization of extensive datasets for training purposes plays a pivotal role in enhancing algorithmic performance. However, it is common for data obtained exclusively from inertial sensors, a Doppler Velocity Log (DVL), and depth sensors in underwater environments to not be publicly accessible. This research paper introduces an underwater navigation dataset derived from a controllable AUV that is equipped with high-precision fiber-optic inertial sensors, a DVL, and depth sensors. The dataset underwent rigorous testing through numerical calculations and optimization-based algorithms, with the evaluation of various algorithms being based on both the actual surfacing position and the calculated position.
AB - Autonomous underwater vehicles (AUVs) are extensively utilized in various autonomous underwater missions, encompassing ocean environment monitoring, underwater searching, and geological exploration. Owing to their profound underwater capabilities and robust autonomy, AUVs have emerged as indispensable instruments. Nevertheless, AUVs encounter several constraints in the domain of underwater navigation, primarily stemming from the cost-intensive nature of inertial navigation devices and Doppler velocity logs, which impede the acquisition of navigation data. Underwater simultaneous localization and mapping (SLAM) techniques, along with other navigation approaches reliant on perceptual sensors like vision and sonar, are employed to augment the precision of self-positioning. Particularly within the realm of machine learning, the utilization of extensive datasets for training purposes plays a pivotal role in enhancing algorithmic performance. However, it is common for data obtained exclusively from inertial sensors, a Doppler Velocity Log (DVL), and depth sensors in underwater environments to not be publicly accessible. This research paper introduces an underwater navigation dataset derived from a controllable AUV that is equipped with high-precision fiber-optic inertial sensors, a DVL, and depth sensors. The dataset underwent rigorous testing through numerical calculations and optimization-based algorithms, with the evaluation of various algorithms being based on both the actual surfacing position and the calculated position.
KW - AUV
KW - dataset
KW - DVL
KW - inertial navigation
KW - underwater navigation
UR - http://www.scopus.com/inward/record.url?scp=85172882195&partnerID=8YFLogxK
U2 - 10.3390/electronics12183788
DO - 10.3390/electronics12183788
M3 - 文章
AN - SCOPUS:85172882195
SN - 2079-9292
VL - 12
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 18
M1 - 3788
ER -