基于相机图像的水下检测位姿估计
Pose Estimation from Camera Images for Underwater Inspection
AI总结：
针对水下复检任务中传统定位方法的挑战与成本问题，探索基于学习的位姿估计器在不同水质下的有效性，创新采用视图合成生成增强数据提升未探索区域性能，并结合扩展卡尔曼滤波器与传感器数据提高定位精度。
摘要：
高精度定位在水下复检任务中至关重要。惯性导航系统、多普勒测速仪和声学定位等传统定位方法面临重大挑战，且对部分应用而言成本效益不高。在这种情况下，视觉定位是一种经济高效的替代方案，它利用检测设备已配备的相机，从周围场景图像中估计位姿。其中，基于机器学习的图像位姿估计在水下环境中展现出潜力，通过基于先前映射场景训练的模型实现高效重定位。我们探索了基于学习的位姿估计器在清澈和浑浊水域检测任务中的有效性，评估了图像格式、模型架构和训练数据多样性的影响。我们通过采用新颖的视图合成模型生成增强训练数据进行创新，显著提升了未探索区域的位姿估计性能。此外，我们通过扩展卡尔曼滤波器将位姿估计器输出与传感器数据集成，提高了定位精度，证明了轨迹平滑度和准确性的提升。
英文摘要：
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 synthesis models to generate augmented training data, significantly enhancing pose estimation in unexplored regions. Moreover, we enhance localization accuracy by integrating pose estimator outputs with sensor data via an extended Kalman filter, demonstrating improved trajectory smoothness and accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
