# DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation

arXiv: https://arxiv.org/abs/2607.23755 · HTML: https://mlsanity.com/paper/2607.23755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem](https://mlsanity.com/paper/1701.08376/) (2017)
- [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019)
- [Selective Sensor Fusion for Neural Visual-Inertial Odometry](https://mlsanity.com/paper/1903.01534/) (2019)
- [Projective Manifold Gradient Layer for Deep Rotation Regression](https://mlsanity.com/paper/2110.11657/) (2021)
- [DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints](https://mlsanity.com/paper/1906.11435/) (2019)
- [Online Temporal Calibration for Monocular Visual-Inertial Systems](https://mlsanity.com/paper/1808.00692/) (2018)
- [SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation](https://mlsanity.com/paper/1911.09968/) (2019)
- [Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection](https://mlsanity.com/paper/2205.06187/) (2022)
- [GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation](https://mlsanity.com/paper/2103.07899/) (2021)
- [SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera](https://mlsanity.com/paper/2207.03704/) (2022)
- [EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention](https://mlsanity.com/paper/2209.08490/) (2022)
- [Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction](https://mlsanity.com/paper/1803.05850/) (2018)
- [Causal Transformer for Fusion and Pose Estimation in Deep Visual Inertial Odometry](https://mlsanity.com/paper/2409.08769/) (2024)
- [UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation](https://mlsanity.com/paper/2409.13106/) (2024)

## Abstract (arXiv)

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 (trel) of 1.31% and rotation error (rrel) of 0.46circ. Furthermore, it accurately estimates poses and maintains robust performance under severe artificially injected temporal misalignment.