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，一种新颖的多鱼眼视觉惯性SLAM系统，通过利用束调整（BA）和循环场变换（RFT）在挑战性场景中实现准确鲁棒的状态估计。首先，系统直接处理原始鱼眼图像，从而充分挖掘鱼眼相机宽视场角（FoV）的优势。其次，为克服低纹理挑战，我们通过统一因子图探索多相机输入与互补惯性测量的紧耦合集成，并联合优化位姿与密集深度图。第三，为保持全局一致性，鱼眼相机的宽视场角使系统能够发现更多潜在回环，而借助RFT的宽收敛域，系统可在极小重叠区域下实现极宽基线回环闭合。此外，我们引入半位姿图BA方法以避免昂贵的全局BA。通过将相对位姿因子与回环因子结合，全局状态可在维持高精度的同时以适度内存开销实现高效调整。在TUM-VI、Hilti-Oxford及Newer College数据集上的评估表明，本系统性能优于现有方法。在2022年Hilti SLAM挑战赛中，我们的VIO版本获得第二名；后续提交的完整系统（含全局BA后端）表现更超越夺冠方案。