DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping
<|reference_start|>DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping: Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability. However, there is a lack of research in fusing these learning-based methods with multi-sensor information, which could be indispensable to push related applications to large-scale and complex scenarios. In this paper, we tightly integrate the trainable deep dense bundle adjustment (DBA) with multi-sensor information through a factor graph. In the framework, recurrent optical flow and DBA are performed among sequential images. The Hessian information derived from DBA is fed into a generic factor graph for multi-sensor fusion, which employs a sliding window and supports probabilistic marginalization. A pipeline for visual-inertial integration is firstly developed, which provides the minimum ability of metric-scale localization and mapping. Furthermore, other sensors (e.g., global navigation satellite system) are integrated for driftless and geo-referencing functionality. Extensive tests are conducted on both public datasets and self-collected datasets. The results validate the superior localization performance of our approach, which enables real-time dense mapping in large-scale environments. The code has been made open-source (https://github.com/GREAT-WHU/DBA-Fusion).<|reference_end|>
@article{zhou2024dba-fusion:,
    title={DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with
  Multiple Sensors for Large-Scale Localization and Mapping},
    author={Yuxuan Zhou, Xingxing Li, Shengyu Li, Xuanbin Wang, Shaoquan Feng,
  Yuxuan Tan},
    journal={arXiv preprint arXiv:2403.13714},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13714},
    primaryClass={cs.RO cs.CV}
}