外观
论文arXiv 2607.237553 个月前
DAP-Pose：用于鲁棒姿态估计的深度时间对齐与物理感知跨模态传感器融合
原文链接：DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation（arXiv 2607.23755 · 发布日期：2026-07-26）
DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation
DAP-Pose通过跨模态融合、潜空间时间对齐和物理约束，在KITTI上验证多模态鲁棒姿态估计。
核心要点
- 面向复杂环境中自动驾驶车辆和移动机器人，提升多模态传感器姿态估计的鲁棒性与精度。
- 提出端到端模型，BCF融合视觉、惯性和GNSS的语义与几何运动线索。
- DTA在潜空间显式对齐异步数据流，无需严格硬件同步即可建模一致运动。
- 在KITTI上达到最低平均平移误差1.31%和旋转误差0.46°，且能应对严重人工时间错位。
验证范围与局限：验证范围为公开KITTI基准数据集及人工注入的时间错位场景。
英文摘要 (Abstract)
Robust and accurate pose estimation with multi-modal sensors is fundamental for autonomous vehicles and mobile robotic systems in complex environments. In this paper, we propose DAP-Pose, a unified end-to-end model for robust multi-modal pose estimation. DAP-Pose introduces a Bi-level Cross-modal Fusion (BCF) module that captures complementary semantic and geometric motion cues from visual, inertial, and GNSS measurements. To handle temporal offsets, we designed a Deep Temporal Alignment (DTA) module that explicitly aligns asynchronous streams in latent space, enabling coherent motion modeling without strict hardware synchronization. Furthermore, we incorporate physics-aware constraints via manifold geometry and GNSS-guided absolute metric scale, enforcing motion consistency and mitigating drift. Experiments upon the public KITTI benchmark dataset were conducted to evaluate the performance of DAP-Pose against existing methods. DAP-Pose achieved the state-of-the-art performance, with the lowest average translation error ($t_{rel}$) of 1.31% and rotation error ($r_{rel}$) of 0.46$^{\circ}$. Furthermore, it accurately estimates poses and maintains robust performance under severe artificially injected temporal misalignment.
作者：Jianhan Lin, Yuchu Qin, Jiateng Yuan, Wenbo Zhang, Shuai Gao
来源
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