# DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping

arXiv: https://arxiv.org/abs/2403.13714 · HTML: https://mlsanity.com/paper/2403.13714/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM](https://mlsanity.com/paper/2304.14377/) (2023)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM](https://mlsanity.com/paper/2302.03594/) (2023)
- [DeepFactors: Real-Time Probabilistic Dense Monocular SLAM](https://mlsanity.com/paper/2001.05049/) (2020)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03642/) (2019)
- [VDO-SLAM: A Visual Dynamic Object-aware SLAM System](https://mlsanity.com/paper/2005.11052/) (2020)
- [Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization](https://mlsanity.com/paper/1804.05625/) (2018)
- [DM-VIO: Delayed Marginalization Visual-Inertial Odometry](https://mlsanity.com/paper/2201.04114/) (2022)
- [DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features](https://mlsanity.com/paper/2008.05416/) (2020)
- [GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation](https://mlsanity.com/paper/2103.07899/) (2021)
- [Improving Monocular Visual Odometry Using Learned Depth](https://mlsanity.com/paper/2204.01268/) (2022)
- [Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas](https://mlsanity.com/paper/2009.02672/) (2020)
- [BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms](https://mlsanity.com/paper/2306.01173/) (2023)
- [DVI-SLAM: A Dual Visual Inertial SLAM Network](https://mlsanity.com/paper/2309.13814/) (2023)

## Built on by (most cited first, 5 of 5)

- [VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes](https://mlsanity.com/paper/2501.08286/) (2025)
- [MASt3R-Fusion: Integrating Feed-Forward Visual Model with IMU, GNSS for High-Functionality SLAM](https://mlsanity.com/paper/2509.20757/) (2025)
- [VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM](https://mlsanity.com/paper/2512.02293/) (2025)
- [SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames](https://mlsanity.com/paper/2412.01500/) (2024)
- [Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometry](https://mlsanity.com/paper/2511.21083/) (2025)

## Abstract (arXiv)

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).