FoundationVIO：在前馈式视觉惯性里程计中强制执行度量尺度
FoundationVIO: Enforcing Metric Scale in Feed-forward Visual-Inertial Odometry
AI总结：
FoundationVIO将惯性传感与几何基础模型（GFM）预测结合，采用因子图方法恢复度量尺度，构建的视觉惯性SLAM系统性能优于采用GFM的SLAM方法，可近似实时运行。
摘要：
前馈几何基础模型（GFM）可直接从图像批次中预测密集几何结构与相机位姿。新兴的视觉SLAM方法将连续的GFM批次串联起来，以构建密集点云地图。核心挑战在于如何解决GFM预测中模糊的几何尺度问题：独立预测在尺度上是局部一致的，但未受任何直接物理测量的约束。在已知尺度数据上训练的模型可提升GFM重建的一致性，但仍无法直接测量尺度。因此，采用GFM的SLAM方法生成的轨迹与3D重建会继承这一问题。受经典视觉惯性里程计的启发，我们提出FoundationVIO，该方法将惯性传感与GFM预测相结合，采用基于因子图的方法恢复度量尺度重建。我们的里程计估计可与经典最先进的VIO方法相媲美，且其准确的密集重建优于采用GFM的SLAM方法。我们还添加了一个极简的基于GFM的回环机制，以构建完整的视觉惯性SLAM系统，该系统可生成全局准确的重建结果与位姿估计。我们在三个配备知名传感器系统的数据集上对所提方法进行评估，并验证其在通用相机与IMU传感器上的兼容性。该方法在消费级笔记本GPU上可实现近似实时运行。代码：this https URL。
英文摘要：
Feed-forward geometric foundation models (GFMs) directly predict dense geometry and camera poses from batches of images. Emerging visual SLAM methods chain together consecutive GFM batches to form dense point cloud maps. A core challenge is how to address the ambiguous geometric scale of GFM predictions: independent predictions are locally consistent up-to-scale but not constrained by any direct physical measurements. Models trained on data with known scale improve the consistency of GFM reconstructions but again do not directly measure it. Consequently, trajectories and 3D reconstructions from GFM-enabled SLAM methods inherit this issue. Motivated by parallels with classical visual-inertial odometry, we introduce FoundationVIO which integrates inertial sensing with GFM predictions to recover metric scale reconstruction using a factor-graph based approach. We demonstrate odometry estimates competitive with classical state-of-the-art VIO methods and accurate dense reconstructions that outperform GFM-enabled SLAM methods. We also add a minimal GFM-based loop-closure mechanism to create a full Visual-Inertial SLAM system capable of creating globally accurate reconstructions and pose estimates. We evaluate our method on three datasets with well known sensor systems and demonstrate its compatibility on ubiquitous camera and IMU sensors. The method runs at approximately real-time on a consumer-grade laptop GPU. Code: https://github.com/ori-drs/FoundationVIO.
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。
