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.

翻译：神经辐射场（NeRF）提出了一种新颖的场景表示方法，能够从二维图像实现高质量的三维重建。在其显著成果的基础上，在NeRF地图中进行全局定位是实现广泛应用的关键任务。最近，Loc-NeRF展示了一种将传统蒙特卡洛定位与NeRF相结合的定位方法，显示出将NeRF用作环境地图的良好前景。然而，尽管取得了进展，Loc-NeRF仍面临耗时较长的光线渲染过程的挑战，这在实际应用中可能是一个重大限制。为了解决这个问题，我们提出了Fast Loc-NeRF，它采用了一种由粗到精的方法，以实现更高效、更准确的基于NeRF地图的全局定位。具体而言，Fast Loc-NeRF在从低到高的多分辨率层级上匹配渲染像素与观测图像。因此，它在保持精确定位结果的同时，加速了昂贵的粒子更新过程。此外，为了剔除异常粒子，我们提出了粒子剔除加权方法，该方法通过利用NeRF的特性来估计粒子的不确定性，并在粒子加权过程中予以考虑。我们的Fast Loc-NeRF在多个基准测试中取得了最新的最优定位性能，证明了其准确性与高效性。