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.

翻译：在动态场景、光照变化和季节更替等复杂因素影响下，利用视觉即时定位与地图构建（SLAM）系统在户外环境中进行导航面临重大挑战，亟需具备鲁棒性的解决方案。传统SLAM方法在适应性方面存在局限，而基于深度学习的方法以及新兴的神经辐射场与高斯溅射SLAM技术提供了有前景的替代方案。然而，现有研究主要集中于条件可控的室内稳定环境进行评估，对其在非结构化、多变的户外场景中的性能认知仍存在空白。本研究通过在多变的自然户外环境中系统评估上述方法，重点关注相机跟踪精度、对环境因素的鲁棒性及计算效率，揭示了不同方法间的显著权衡关系。大量实验表明，神经SLAM方法（尤其在弱光等挑战性条件下）展现出更优的鲁棒性，但需付出高昂的计算代价；同时，传统方法在跨季节场景中表现最佳，但对光照条件变化极为敏感。本基准测试的代码已公开于https://github.com/iis-esslingen/nerf-3dgs-benchmark。