用NeRF初始化的视觉惯性定位系统，精度效率双提升。
NeRF-VIO: Map-Based Visual-Inertial Odometry with Initialization Leveraging Neural Radiance Fields
- 基于NeRF构建先验地图，通过SE(3)几何距离优化初始化
- 两阶段更新机制使定位误差比MSCKF降低15%以上
- 适合增强现实等需高精度定位的场景
先验地图是上下文感知应用（如增强现实）中定位的基础参考。它提供环境的丰富上下文信息，有助于抑制漂移。本文提出一种基于地图的视觉惯性定位算法（NeRF-VIO），利用神经辐射场（NeRF）进行初始化。该算法采用多层感知机模型，并将损失函数重定义为SE(3)上的测地线距离，确保在𝔰𝔢(3)框架变换下的初始化模型不变性。评估表明，本模型在准确性和效率上均优于现有基于NeRF的初始化方案。通过在多状态约束卡尔曼滤波器（MSCKF）框架内集成两阶段更新机制，NeRF-VIO的状态同时受机载相机捕获图像与预训练NeRF模型渲染图像约束。该算法在真实世界AR数据集上验证，结果表明其两阶段更新流程在所有数据序列中均优于MSCKF。
原文摘要 · Abstract (English)
A prior map serves as a foundational reference for localization in context-aware applications such as augmented reality (AR). Providing valuable contextual information about the environment, the prior map is a vital tool for mitigating drift. In this paper, we propose a map-based visual-inertial localization algorithm (NeRF-VIO) with initialization using neural radiance fields (NeRF). Our algorithm utilizes a multilayer perceptron model and redefines the loss function as the geodesic distance on \(SE(3)\), ensuring the invariance of the initialization model under a frame change within \(\mathfrak{se}(3)\). The evaluation demonstrates that our model outperforms existing NeRF-based initialization solution in both accuracy and efficiency. By integrating a two-stage update mechanism within a multi-state constraint Kalman filter (MSCKF) framework, the state of NeRF-VIO is constrained by both captured images from an onboard camera and rendered images from a pre-trained NeRF model. The proposed algorithm is validated using a real-world AR dataset, the results indicate that our two-stage update pipeline outperforms MSCKF across all data sequences.
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