Pose Estimation from Camera Images for Underwater Inspection
Luyuan Peng, Hari Vishnu, Mandar Chitre, Yuen Min Too, Bharath Kalyan, Rajat Mishra, Soo Pieng Tan

TL;DR
This paper investigates learning-based visual pose estimation for underwater inspection, enhancing accuracy with novel data augmentation and sensor fusion, offering a cost-effective alternative to traditional localization methods.
Contribution
It introduces a novel approach combining view synthesis for data augmentation and sensor fusion via Kalman filtering to improve underwater pose estimation accuracy.
Findings
Enhanced pose accuracy in turbid water environments.
Significant improvement using augmented training data.
Effective integration of visual and sensor data.
Abstract
High-precision localization is pivotal in underwater reinspection missions. Traditional localization methods like inertial navigation systems, Doppler velocity loggers, and acoustic positioning face significant challenges and are not cost-effective for some applications. Visual localization is a cost-effective alternative in such cases, leveraging the cameras already equipped on inspection vehicles to estimate poses from images of the surrounding scene. Amongst these, machine learning-based pose estimation from images shows promise in underwater environments, performing efficient relocalization using models trained based on previously mapped scenes. We explore the efficacy of learning-based pose estimators in both clear and turbid water inspection missions, assessing the impact of image formats, model architectures and training data diversity. We innovate by employing novel view…
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