发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对视觉惯性里程计问题，提出DB-VIO双分支框架，通过融入深度线索、姿态先验和解耦姿态估计进行运动特定时间建模，实验表明该方法在自动驾驶和空中机器人基准测试中性能优异，提升了旋转和平移估计精度。
AI 中文摘要
视觉惯性里程计（VIO）对于移动机器人系统中精确的6自由度运动估计至关重要。最近基于学习的VIO方法取得了进展，但依赖统一表示和单一模型，限制了对旋转和平移动态的捕捉。单目视觉特征缺乏几何结构，原始惯性编码使旋转运动学隐含。为此提出DB-VIO双分支框架，融入深度线索、注入姿态先验并解耦姿态估计。实验表明其在自动驾驶和空中机器人基准测试中达到最优性能，在KITTI和EuRoC上分别提升20%和33%，在EuRoC更敏捷运动模式下旋转指标提升65.7%，证明了其有效性和泛化性。
英文摘要
Visual inertial odometry (VIO) is essential for accurate 6-DoF motion estimation in mobile robotic systems. Recent learning-based VIO methods have shown promising progress, but they often rely on unified visual--inertial representations and a single temporal model for full-pose estimation, limiting their ability to capture the heterogeneous dynamics of rotation and translation. Moreover, monocular visual features often lack explicit geometric structure, while raw inertial encoding leaves the underlying rotational kinematics implicit, weakening the rotation-related cues in IMU features. To address these issues, we propose DB-VIO, a dual-branch visual inertial odometry framework with enhanced visual--inertial representation. DB-VIO incorporates depth cues to improve monocular visual perception, injects an explicit integrated-attitude prior to strengthen rotation-aware inertial representation, and decouples pose estimation into dedicated rotational and translational branches for motion-specific temporal modeling. Experiments on autonomous driving and aerial robot benchmarks show that DB-VIO achieves state-of-the-art performance, improving the corresponding baselines by 20\% on KITTI and 33\% on EuRoC. Notably, under the more agile motion patterns of EuRoC, DB-VIO improves the rotational metric by 65.7\% over prior methods. These results demonstrate the effectiveness and generalization of DB-VIO across different platforms and motion scenarios.
