NeRF and Gaussian Splatting SLAM in the Wild

TL;DR

This paper evaluates neural radiance field and Gaussian Splatting SLAM methods in outdoor environments, revealing their robustness and computational trade-offs compared to traditional approaches.

Contribution

It provides the first comprehensive outdoor evaluation of neural SLAM methods, highlighting their strengths and limitations in unstructured, variable conditions.

Findings

Neural SLAM methods are more robust in challenging outdoor conditions.

Traditional methods perform best across seasons but are sensitive to lighting changes.

Neural methods have higher computational costs.

Abstract

Navigating outdoor environments with visual Simultaneous Localization and Mapping (SLAM) systems poses significant challenges due to dynamic scenes, lighting variations, and seasonal changes, requiring robust solutions. While traditional SLAM methods struggle with adaptability, deep learning-based approaches and emerging neural radiance fields as well as Gaussian Splatting-based SLAM methods, offer promising alternatives. However, these methods have primarily been evaluated in controlled indoor environments with stable conditions, leaving a gap in understanding their performance in unstructured and variable outdoor settings. This study addresses this gap by evaluating these methods in natural outdoor environments, focusing on camera tracking accuracy, robustness to environmental factors, and computational efficiency, highlighting distinct trade-offs. Extensive evaluations demonstrate that neural SLAM methods achieve superior robustness, particularly under challenging conditions such as low light, but at a high computational cost. At the same time, traditional methods perform the best across seasons but are highly sensitive to variations in lighting conditions. The code of the benchmark is publicly available at https://github.com/iis-esslingen/nerf-3dgs-benchmark.