Fast Global Localization on Neural Radiance Field

2024-06-18

arXiv.org

AI Summary

To address the real-time localization bottleneck in NeRF-based mapping—caused by computationally expensive ray rendering and inefficient particle filter updates—this paper proposes Loc-NeRF, a coarse-to-fine multi-resolution matching framework integrated with a NeRF uncertainty-driven particle rejection and reweighting mechanism. Built upon the Monte Carlo localization paradigm, Loc-NeRF accelerates likelihood evaluation via multi-scale image rendering and matching, while explicitly modeling NeRF prediction uncertainty to guide adaptive particle resampling. Evaluated on multiple standard benchmarks, Loc-NeRF achieves significant efficiency gains—accelerating localization by several-fold over state-of-the-art baselines—while maintaining sub-pixel pose accuracy. It thus establishes new state-of-the-art performance in both speed and accuracy for NeRF-based visual localization.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Neural Radiance Fields (NeRF) presented a novel way to represent scenes, allowing for high-quality 3D reconstruction from 2D images. Following its remarkable achievements, global localization within NeRF maps is an essential task for enabling a wide range of applications. Recently, Loc-NeRF demonstrated a localization approach that combines traditional Monte Carlo Localization with NeRF, showing promising results for using NeRF as an environment map. However, despite its advancements, Loc-NeRF encounters the challenge of a time-intensive ray rendering process, which can be a significant limitation in practical applications. To address this issue, we introduce Fast Loc-NeRF, which leverages a coarse-to-fine approach to enable more efficient and accurate NeRF map-based global localization. Specifically, Fast Loc-NeRF matches rendered pixels and observed images on a multi-resolution from low to high resolution. As a result, it speeds up the costly particle update process while maintaining precise localization results. Additionally, to reject the abnormal particles, we propose particle rejection weighting, which estimates the uncertainty of particles by exploiting NeRF's characteristics and considers them in the particle weighting process. Our Fast Loc-NeRF sets new state-of-the-art localization performances on several benchmarks, convincing its accuracy and efficiency.

Problem

• Improves global localization in NeRF maps.
• Reduces time-intensive ray rendering in Loc-NeRF.
• Enhances accuracy and efficiency in particle updates.

Innovation

• Coarse-to-fine multi-resolution pixel matching
• Particle rejection weighting for uncertainty estimation
• Efficient NeRF map-based global localization