NeRF-VIO: Map-Based Visual-Inertial Odometry with Initialization Leveraging Neural Radiance Fields (2503.07952v1)

Abstract: 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.

An Analysis of NeRF-VIO: Map-Based Visual-Inertial Odometry

This paper presents a novel approach termed NeRF-VIO, which integrates map-based visual-inertial odometry with the use of neural radiance fields (NeRF) for initialization. The key contribution of this work is the development of a localization algorithm that leverages a multilayer perceptron (MLP) model, redefining the initialization loss function in terms of the geodesic distance on the Lie group $SE(3)$, thus ensuring frame invariance under transformations in $\mathfrak{se}(3)$.

Technical Overview

NeRF-VIO addresses the localization drift problem in visual-inertial navigation systems (VINS) by using a prior map generated from neural radiance fields. The prior map adds contextual information that enhances the global consistency of localizationespecially in cases where alternative global location information, such as GNSS data or loop closure techniques, is unavailable. The main technical innovation lies in merging real-time onboard camera images with rendered images from a pre-trained NeRF model to update localization.

The authors propose a two-stage update mechanism integrated within a multi-state constraint Kalman filter (MSCKF) framework. Evaluation on real-world datasets demonstrates that this method consistently outperforms existing solutions by achieving higher accuracy and efficiency.

Numerical Results and Validation

The quantitative assessments indicate that the NeRF-VIO algorithm achieves superior performance over both traditional MSCKF methods and previous NeRF-based solutions in accuracy and computational efficiency. The initialization accuracy, evaluated in terms of $L_2$ norms of orientation and position errors, showed significant improvements over previous methods like iNeRF, demonstrating resilience to initialization guess quality—a notable advancement in practical applicability.

In real-world sequences, NeRF-VIO achieves a lower absolute trajectory error (ATE) in comparison to standard MSCKF, showing robustness in performance in varied environmental contexts. Additionally, the rendering quality of NeRF models was validated with test datasets, showing a high level of detail and correctness after sufficient training iterations.

Practical and Theoretical Implications

On the practical front, NeRF-VIO extends the applicability of VINS to more complex and diverse operational scenarios, potentially transforming AR/VR experiences by providing seamless localization and tracking capabilities with low latency. Theoretically, the redefinition of the initialization loss function using geodesic distances introduces a robust framework for pose estimation, leveraging the mathematical properties of Lie groups for improved consistency across frames.

Future work in this domain could explore extending these techniques to dynamic environments or incorporating additional sensor modalities to further improve robustness and scalability. The potential cross-pollination of ideas from NeRF and SLAM could lead to richer, more detailed mapping and localization systems.

In summary, the NeRF-VIO algorithm represents an integration of computational geometry, neural networks, and real-time systems to address significant challenges in visual-inertial odometry—a promising direction for future research and application development.