Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields (2209.09050v1)

Abstract: We present Loc-NeRF, a real-time vision-based robot localization approach that combines Monte Carlo localization and Neural Radiance Fields (NeRF). Our system uses a pre-trained NeRF model as the map of an environment and can localize itself in real-time using an RGB camera as the only exteroceptive sensor onboard the robot. While neural radiance fields have seen significant applications for visual rendering in computer vision and graphics, they have found limited use in robotics. Existing approaches for NeRF-based localization require both a good initial pose guess and significant computation, making them impractical for real-time robotics applications. By using Monte Carlo localization as a workhorse to estimate poses using a NeRF map model, Loc-NeRF is able to perform localization faster than the state of the art and without relying on an initial pose estimate. In addition to testing on synthetic data, we also run our system using real data collected by a Clearpath Jackal UGV and demonstrate for the first time the ability to perform real-time global localization with neural radiance fields. We make our code publicly available at https://github.com/MIT-SPARK/Loc-NeRF.

• The paper introduces a novel framework that integrates Neural Radiance Fields as maps within a Monte Carlo Localization paradigm, eliminating the need for initial pose estimates.
• The methodology employs prediction, update, and resampling steps with particle filtering and annealing to enhance localization accuracy and efficiency.
• Experimental results in single-image, pose tracking, and real-time scenarios demonstrate superior performance over existing NeRF-based localization methods.

Overview of Loc-NeRF: Integration of Monte Carlo Localization and Neural Radiance Fields

The presented paper introduces Loc-NeRF, a system designed for real-time robot localization that leverages Monte Carlo Localization (MCL) in conjunction with Neural Radiance Fields (NeRF). NeRF, traditionally used in the domains of computer vision and graphics for visual rendering, is employed here as a map for robotic localization. The paper discerns itself by integrating NeRF within the MCL framework to facilitate accurate robot localization using only an RGB camera, significantly easing computational overheads compared to previous approaches.

Methodology and Key Contributions

The core contribution of Loc-NeRF is a novel utilization of NeRF as a map model within a particle-filter-based MCL paradigm. The approach does not rely on an initial pose estimate, unlike previous NeRF-based localization methods. By structuring the robot's environment as a NeRF map, the system can evaluate particle weights using NeRF-rendered expected views versus real captured images.

The paper details key components of the Loc-NeRF framework:

• Prediction Step: Utilizes motion estimates from visual-inertial odometry or integrates dynamics to predict particle movements.
• Update Step: Adjusts particle weights by comparing rendered images from the NeRF map to observed images, increasing or reducing weights based on similarity.
• Resampling Step: Reinforces particles with accurate estimates while culling those with less likely poses.

Innovations like particle annealing, which dynamically adjusts the prediction noise and number of particles, significantly improve localization efficiency and computational load.

Results and Evaluation

The Loc-NeRF system was evaluated against the state-of-the-art iNeRF and NeRF-Navigation approaches across three experimental setups:

1. Single-Image Localization: Demonstrations using LLFF datasets show Loc-NeRF's capability to localize effectively from poorly guessed initial positions.
2. Pose Tracking: Tests using synthetic data from Blender indicate Loc-NeRF's superior accuracy over NeRF-Navigation, with robustness to low-quality NeRF renderings.
3. Real-Time Demonstration: The system was successfully deployed on a Clearpath Jackal UGV equipped with a Realsense d455 camera, showcasing real-world feasibility.

Future improvements could expand on these results by integrating adaptive particle techniques for further computational optimizations and exploring extensive NeRF models for larger environments.

Implications and Future Directions

Practically, Loc-NeRF opens the door for robust, efficient robot localization in complex and dynamic environments using simplified sensory setups. Theoretically, it underlines potential new applications of NeRF in robotics, transcending its conventional use cases in visual rendering. Given the potential integration with enhanced particle filtering techniques and optimized NeRF training frameworks, Loc-NeRF lays a foundation for future research in scalable robotic localization and real-time scene understanding. Further developments may involve testing on more extensive scenes and enhancing the real-time capabilities with faster NeRF rendering algorithms.