外观
论文arXiv 2609.277021 个月前
DAVIO：带前馈初始化和位姿条件建图的稠密单目-惯性SLAM
原文链接：DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping（arXiv 2609.27702 · 发布日期：2026-09-23）
DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping
DAVIO面向单目-惯性度量定位与稠密建图，借助Depth Anything 3完成启动和位姿条件建图，并在EuRoC与ORI序列验证。
核心要点
- 问题/动机：经典视觉惯性滤波需等待视差且仅保留稀疏地标，前馈模型缺少尺度和重力。
- 方法：用Depth Anything 3同时服务启动与建图，五图像窗口结合预积分IMU构成无特征线性系统。
- 实验：在EuRoC更早启动、降低定位误差并提升建图精度；在ORI上与SOTA持平或更好。
- 开源：作者发布DAVIO代码，定位为实时稠密度量SLAM系统。
验证范围与局限：验证限于EuRoC与建筑尺度ORI序列上的单目相机+IMU设置。
英文摘要 (Abstract)
A camera and an IMU are the minimal sensor setup for metric localization and dense mapping, yet classical visual--inertial filters must wait for parallax before they start and then retain only sparse landmarks. Feed-forward geometry models, in contrast, predict dense structure from a few images but provide neither metric scale nor gravity. We present DAVIO, which uses a single multi-view depth model, Depth Anything~3, for both start-up and mapping. At start-up, a five-image window and preintegrated IMU measurements form a feature-free linear system. Its robust, conditioning-checked solution bootstraps a VIO filter through buffered replay. During tracking, the filter's metric poses condition the depth model. Residual scale is corrected only along viewing rays, which preserves the metric camera baselines, and a gravity-preserving submap graph with drift-gated revisits refines the map. On EuRoC, DAVIO starts markedly earlier, reduces the localization error, and maps more accurately than SOTA feed-forward mappers given identical poses. On building-scale ORI sequences, DAVIO is on bar or better than SOTA mappers on the same odometry, and degrades far less when GT poses are replaced by real odometry. We release the code of DAVIO, a real-time dense metric SLAM system, to the community.
作者：Jaafar Mahmoud, Arthur Movsesyan, Mikhail Iumanov, Sergey Kolyubin
来源
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