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

Основні поняття

A novel framework that integrates NeRF-derived localization information with Visual-Inertial Odometry to provide a robust solution for robotic navigation in real-time, by training an absolute pose regression network with augmented image data rendered from a NeRF and quantifying its uncertainty.

Анотація

The paper introduces a new framework that combines Neural Radiance Fields (NeRF) and Visual-Inertial Odometry (VIO) to achieve robust and real-time robotic navigation.
Key highlights:

NeRF is used to generate a large dataset of synthetic images and poses, which are then used to train a camera pose regressor network. This regressor provides absolute pose estimates to counter drift in the VIO system.
Uncertainty quantification is incorporated into the pose regressor using Bayesian neural network techniques like Monte Carlo dropout and deep ensembles. This allows the VIO system to reason about the reliability of the pose estimates.
The pose regressor outputs and their uncertainties are integrated into the VIO framework through a Bayesian optimization approach, enabling robust navigation without the need for loop closure.
Experiments in a photorealistic simulation environment demonstrate significant improvements in accuracy compared to a conventional VIO approach, while maintaining computational feasibility for onboard systems.

Статистика

The paper presents the following key metrics and figures:

The proposed framework achieves a 44.5% improvement in average positional accuracy over a baseline VIO-only approach.
Incorporating uncertainty quantification and outlier rejection further enhances the positional accuracy, while also maintaining comparable rotational accuracy to the VIO-only baseline.
The inference speed of the camera pose regressor ranges from 8-41 ms on a Jetson AGX Xavier embedded system, enabling real-time operation.

Цитати

"We propose a real-time and loop closure-free VIO framework leveraging information extracted from an imperfectly trained NeRF that can be feasibly run on embedded hardware."
"We mathematically formulate the integration between VIO and the uncertain poses estimated by a neural network as Maximum a Posteriori (MAP) optimization in a Bayesian setting."
"We analyze the accuracy and efficiency improvements of the proposed framework compared to baselines."

Ключові висновки, отримані з

NVINS

by Juyeop Han,L... о arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01400.pdf

Глибші Запити

How can the proposed framework be extended to handle dynamic environments and long-term changes in the scene representation over time

To extend the proposed framework to handle dynamic environments and long-term changes in scene representation over time, several strategies can be implemented. Firstly, incorporating a mechanism for continuous retraining of the NeRF model based on new data collected from the environment can help adapt to changes. This retraining process can be triggered periodically or when significant changes are detected in the scene. Additionally, integrating a mechanism for online learning or incremental updates to the camera pose regressor based on real-time data can enhance adaptability to dynamic environments. By continuously updating the neural networks with new information, the system can better handle variations in the scene representation over time. Furthermore, implementing a mechanism for outlier detection and correction based on discrepancies between the predicted poses and actual measurements can help mitigate errors caused by dynamic changes in the environment. By identifying and correcting outliers in real-time, the system can maintain accuracy and reliability in dynamic scenarios.

What are the potential limitations of the NeRF-based approach, and how can they be addressed to further improve the robustness and reliability of the navigation system

The NeRF-based approach, while powerful for 3D reconstruction and novel view synthesis, has certain limitations that need to be addressed to further improve the robustness and reliability of the navigation system. One limitation is the computational cost associated with NeRF rendering, which can hinder real-time performance, especially on embedded systems. To address this, optimization techniques such as model compression, quantization, or hardware acceleration can be employed to reduce the computational overhead. Another limitation is the potential for artifacts and reconstruction errors in the NeRF representation, which can impact the accuracy of the camera pose regressor. Improving the training data quality, augmenting the dataset with diverse scenarios, and enhancing the neural network architecture can help mitigate these issues. Additionally, incorporating mechanisms for uncertainty quantification and outlier rejection, as demonstrated in the proposed framework, can enhance the system's robustness by accounting for uncertainties in the pose estimates and detecting and correcting outliers effectively.

Could the uncertainty quantification techniques used in this work be applied to other sensor modalities or perception tasks in robotics to enhance overall system performance

The uncertainty quantification techniques used in this work can indeed be applied to other sensor modalities or perception tasks in robotics to enhance overall system performance. By incorporating uncertainty estimation into the processing of sensor data, the system can make more informed decisions and adapt to varying conditions. For example, in the context of LiDAR-based perception tasks, uncertainty quantification can help in identifying unreliable measurements and improving the accuracy of object detection and localization. Similarly, in the case of radar sensors, uncertainty estimation can aid in distinguishing between noise and actual signals, leading to more reliable object tracking. By integrating uncertainty quantification techniques across different sensor modalities, the robotics system can achieve a more comprehensive understanding of its environment and make more robust decisions based on the level of confidence in the sensor data.