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.

翻译：本文提出BAMF-SLAM——一种新型多鱼眼视觉惯性SLAM系统，通过联合利用束调整与递归场变换，在挑战性场景中实现高精度鲁棒的状态估计。首先，系统直接处理原始鱼眼图像，充分挖掘鱼眼相机的宽视场优势。其次，为克服弱纹理挑战，我们探索通过统一因子图紧密耦合多相机输入与互补惯性测量，并联合优化位姿与稠密深度图。第三，在全局一致性方面，鱼眼相机的宽视场使系统能检测更多潜在回环，依托递归场变换的广阔收敛域，系统可在极小重叠条件下完成超宽基线回环闭合。此外，我们提出半位姿图束调整方法以避免代价高昂的全局束调整：通过联合相对位姿因子与回环闭合因子，系统可在保持高精度的同时，以适度内存开销高效调整全局状态。在TUM-VI、Hilti-Oxford与Newer College数据集上的评估表明，本系统性能显著优于现有方法。在2022年Hilti SLAM挑战赛中，本系统的视觉惯性里程计版本取得第二名；后续提交的完整系统（含全局束调整后端）则超越了冠军方案。