Robust Simultaneous Localization and Mapping (SLAM) is a crucial enabler for autonomous navigation in natural, unstructured environments such as parks and gardens. However, these environments present unique challenges for SLAM due to frequent seasonal changes, varying light conditions, and dense vegetation. These factors often degrade the performance of visual SLAM algorithms originally developed for structured urban environments. To address this gap, we present ROVER, a comprehensive benchmark dataset tailored for evaluating visual SLAM algorithms under diverse environmental conditions and spatial configurations. We captured the dataset with a robotic platform equipped with monocular, stereo, and RGB-D cameras, as well as inertial sensors. It covers 39 recordings across five outdoor locations, collected through all seasons and various lighting scenarios, i.e., day, dusk, and night with and without external lighting. With this novel dataset, we evaluate several traditional and deep learning-based SLAM methods and study their performance in diverse challenging conditions. The results demonstrate that while stereo-inertial and RGB-D configurations generally perform better under favorable lighting and moderate vegetation, most SLAM systems perform poorly in low-light and high-vegetation scenarios, particularly during summer and autumn. Our analysis highlights the need for improved adaptability in visual SLAM algorithms for outdoor applications, as current systems struggle with dynamic environmental factors affecting scale, feature extraction, and trajectory consistency. This dataset provides a solid foundation for advancing visual SLAM research in real-world, natural environments, fostering the development of more resilient SLAM systems for long-term outdoor localization and mapping. The dataset and the code of the benchmark are available under https://iis-esslingen.github.io/rover.

翻译：鲁棒的同步定位与建图（SLAM）是实现公园、花园等自然非结构化环境中自主导航的关键技术。然而，此类环境因频繁的季节变化、多变的光照条件以及茂密的植被，给SLAM系统带来了独特挑战。这些因素常会降低原本为结构化城市环境设计的视觉SLAM算法的性能。为填补这一空白，我们提出了ROVER——一个专为评估视觉SLAM算法在多样化环境条件与空间配置下性能而构建的综合基准数据集。我们通过搭载单目、双目和RGB-D相机以及惯性传感器的机器人平台采集数据。该数据集涵盖五个户外场景的39段记录，采集范围覆盖所有季节及多种光照场景（包括白天、黄昏、夜间有/无外部照明）。基于这一全新数据集，我们对多种传统及基于深度学习的SLAM方法进行了评估，并研究了它们在多种挑战性条件下的表现。结果表明，尽管双目-惯性及RGB-D配置在光照良好且植被适中的条件下通常表现更优，但大多数SLAM系统在低光照与高植被密度场景（尤其在夏秋季节）中性能显著下降。我们的分析指出，当前系统在应对影响尺度估计、特征提取与轨迹一致性的动态环境因素时存在不足，凸显了户外应用场景中视觉SLAM算法需提升适应能力的迫切需求。本数据集为推进真实自然环境中视觉SLAM研究奠定了坚实基础，有助于开发更具鲁棒性的长期户外定位与建图系统。数据集与基准测试代码已公开于：https://iis-esslingen.github.io/rover。