# BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms

arXiv: https://arxiv.org/abs/2306.01173 · HTML: https://mlsanity.com/paper/2306.01173/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2023.

## Built on by (most cited first, 11 of 11)

- [Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera](https://mlsanity.com/paper/2501.02464/) (2025)
- [HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields](https://mlsanity.com/paper/2310.04787/) (2023)
- [HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction](https://mlsanity.com/paper/2411.17982/) (2024)
- [EgoHDM: An Online Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping System](https://mlsanity.com/paper/2409.00343/) (2024)
- [DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping](https://mlsanity.com/paper/2403.13714/) (2024)
- [MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping](https://mlsanity.com/paper/2509.14191/) (2025)
- [A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment](https://mlsanity.com/paper/2604.23704/) (2026)
- [VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis](https://mlsanity.com/paper/2311.05289/) (2023)
- [Multi-LVI-SAM: A Robust LiDAR-Visual-Inertial Odometry for Multiple Fisheye Cameras](https://mlsanity.com/paper/2509.05740/) (2025)
- [MRASfM: Multi-Camera Reconstruction and Aggregation through Structure-from-Motion in Driving Scenes](https://mlsanity.com/paper/2510.15467/) (2025)
- [MFVINS: Multiple Fisheye Camera-Based Visual Inertial System](https://mlsanity.com/paper/2609.08626/) (2026)

## Abstract (arXiv)

In this paper, we present BAMF-SLAM, a novel multi-fisheye visual-inertial SLAM system that utilizes Bundle Adjustment (BA) and recurrent field transforms (RFT) to achieve accurate and robust state estimation in challenging scenarios. First, our system directly operates on raw fisheye images, enabling us to fully exploit the wide Field-of-View (FoV) of fisheye cameras. Second, to overcome the low-texture challenge, we explore the tightly-coupled integration of multi-camera inputs and complementary inertial measurements via a unified factor graph and jointly optimize the poses and dense depth maps. Third, for global consistency, the wide FoV of the fisheye camera allows the system to find more potential loop closures, and powered by the broad convergence basin of RFT, our system can perform very wide baseline loop closing with little overlap. Furthermore, we introduce a semi-pose-graph BA method to avoid the expensive full global BA. By combining relative pose factors with loop closure factors, the global states can be adjusted efficiently with modest memory footprint while maintaining high accuracy. Evaluations on TUM-VI, Hilti-Oxford and Newer College datasets show the superior performance of the proposed system over prior works. In the Hilti SLAM Challenge 2022, our VIO version achieves second place. In a subsequent submission, our complete system, including the global BA backend, outperforms the winning approach.