BAMF-SLAM：使用循环场变换的束调整多鱼眼视觉惯性SLAM
BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms
AI总结：
提出BAMF-SLAM多鱼眼视觉惯性SLAM系统，结合束调整与循环场变换，实现低纹理场景下高精度鲁棒状态估计，并在多个数据集及Hilti挑战赛中取得领先性能。
摘要：
本文提出了BAMF-SLAM，一种新颖的多鱼眼视觉惯性SLAM系统，该系统利用束调整（BA）和循环场变换（RFT）在具有挑战性的场景中实现准确且鲁棒的状态估计。首先，我们的系统直接对原始鱼眼图像进行操作，从而能够充分利用鱼眼相机的宽视场（FoV）。其次，为了克服低纹理挑战，我们通过统一的因子图探索多相机输入与互补惯性测量的紧耦合集成，并联合优化位姿和稠密深度图。第三，为了实现全局一致性，鱼眼相机的宽视场使系统能够找到更多潜在的闭环，并且借助RFT的宽收敛域，我们的系统能够在重叠很少的情况下执行非常宽基线的闭环。此外，我们引入了一种半位姿图BA方法，以避免昂贵的完整全局BA。通过将相对位姿因子与闭环因子相结合，全局状态可以在适度的内存占用下高效调整，同时保持高精度。在TUM-VI、Hilti-Oxford和Newer College数据集上的评估表明，所提出的系统优于先前的工作。在Hilti SLAM挑战赛2022中，我们的VIO版本获得第二名。在随后的提交中，我们包含全局BA后端的完整系统超越了获胜方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
