# NVINS: Robust Visual Inertial Navigation Fused with NeRF-augmented Camera Pose Regressor and Uncertainty Quantification

arXiv: https://arxiv.org/abs/2404.01400 · HTML: https://mlsanity.com/paper/2404.01400/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [Neural Fields in Robotics: A Survey](https://mlsanity.com/paper/2410.20220/) (2024)
- [Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation](https://mlsanity.com/paper/2509.01583/) (2025)
- [PCM-NeRF: Probabilistic Camera Modeling for Neural Radiance Fields under Pose Uncertainty](https://mlsanity.com/paper/2604.17831/) (2026)

## Abstract (arXiv)

In recent years, Neural Radiance Fields (NeRF) have emerged as a powerful tool for 3D reconstruction and novel view synthesis. However, the computational cost of NeRF rendering and degradation in quality due to the presence of artifacts pose significant challenges for its application in real-time and robust robotic tasks, especially on embedded systems. This paper introduces a novel framework that integrates NeRF-derived localization information with Visual-Inertial Odometry (VIO) to provide a robust solution for real-time robotic navigation. By training an absolute pose regression network with augmented image data rendered from a NeRF and quantifying its uncertainty, our approach effectively counters positional drift and enhances system reliability. We also establish a mathematically sound foundation for combining visual inertial navigation with camera localization neural networks, considering uncertainty under a Bayesian framework. Experimental validation in a photorealistic simulation environment demonstrates significant improvements in accuracy compared to a conventional VIO approach.