DIVO：用于无人水下航行器的连续时间深度视觉惯性里程计
DIVO: Continuous-time DVL-Inertial-Visual Odometry for Unmanned Underwater Vehicles
AI总结：
针对无人水下航行器视觉定位与建图挑战，提出基于高斯过程的连续时间轨迹估计框架，融合多传感器异步测量，含新视觉前端，在真实数据集测试中性能优于现有算法。
摘要：
本文提出一种新颖的声学视觉惯性里程计解决方案，利用连续时间轨迹估计框架用于无人水下航行器。水下环境给视觉定位和建图带来独特挑战，促使使用额外传感模态和鲁棒视觉跟踪管道。所提系统是首个基于高斯过程融合多传感器异步测量的连续时间轨迹估计框架，还有新视觉前端，经测试性能优于现有算法。
英文摘要：
This paper presents a novel acoustic-visual-inertial odometry solution leveraging a continuous-time trajectory estimation framework for unmanned underwater vehicles. Underwater environments present unique challenges for visual localization and mapping, such as light attenuation, illumination variance, and the presence of particulate matter. This motivates the use of additional sensing modalities and a visual tracking pipeline that is robust to diverse subsea conditions. The proposed system is the first continuous-time trajectory estimation framework based on Gaussian processes to fuse asynchronous measurements from a Doppler velocity log, a stereo camera, and an inertial measurement unit. Additionally, a novel visual frontend is proposed, incorporating learning-based feature extraction and matching that is robust to the specific challenges that subsea environments present. The proposed framework enables seamless integration of additional sensor modalities in continuous-time and is adaptable to different environments without reconfiguration. The proposed system is extensively tested on real-world underwater inspection datasets, where it outperforms state-of-the-art visual-inertial and acoustic-visual-inertial SLAM algorithms in accuracy, robustness, and trajectory coverage. Notably, the proposed system outperforms the state-of-the-art despite only forming short-term visual data associations.
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。
