# ROVER: A Multi-Season Dataset for Visual SLAM arXiv: https://arxiv.org/abs/2412.02506 ยท HTML: https://mlsanity.com/paper/2412.02506/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 71 references that are in the map. - First posted on arXiv 3 Dec 2024. ## Builds on (most cited first, 30 of 36) - [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020) - [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021) - [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020) - [Deep Patch Visual Odometry](https://mlsanity.com/paper/2208.04726/) (2022) - [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020) - [Boreas: A Multi-Season Autonomous Driving Dataset](https://mlsanity.com/paper/2203.10168/) (2022) - [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024) - [Deep Patch Visual SLAM](https://mlsanity.com/paper/2408.01654/) (2024) - [The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth](https://mlsanity.com/paper/2003.05691/) (2020) - [4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving](https://mlsanity.com/paper/2009.06364/) (2020) - [TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models](https://mlsanity.com/paper/2205.01791/) (2022) - [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019) - [The GOOSE Dataset for Perception in Unstructured Environments](https://mlsanity.com/paper/2310.16788/) (2023) - [EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes](https://mlsanity.com/paper/2011.04389/) (2020) - [A Cross-Season Correspondence Dataset for Robust Semantic Segmentation](https://mlsanity.com/paper/1903.06916/) (2019) - [SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments](https://mlsanity.com/paper/2307.07607/) (2023) - [TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks](https://mlsanity.com/paper/2402.01913/) (2024) - [BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments](https://mlsanity.com/paper/2306.14137/) (2023) - [Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning](https://mlsanity.com/paper/2008.05058/) (2020) - [Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning](https://mlsanity.com/paper/2203.01578/) (2022) - [Multi-Camera LiDAR Inertial Extension to the Newer College Dataset](https://mlsanity.com/paper/2112.08854/) (2021) - [WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments](https://mlsanity.com/paper/2312.15364/) (2023) - [FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms](https://mlsanity.com/paper/2208.11865/) (2022) - [FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments](https://mlsanity.com/paper/2404.08563/) (2024) - [EU Long-term Dataset with Multiple Sensors for Autonomous Driving](https://mlsanity.com/paper/1909.03330/) (2019) - [The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments](https://mlsanity.com/paper/1809.06413/) (2018) - [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022) - [LiDAR-as-Camera for End-to-End Driving](https://mlsanity.com/paper/2206.15170/) (2022) - [EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments](https://mlsanity.com/paper/2410.22200/) (2024) - [FieldSAFE: Dataset for Obstacle Detection in Agriculture](https://mlsanity.com/paper/1709.03526/) (2017) ## Built on by (most cited first, 4 of 4) - [Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments](https://mlsanity.com/paper/2503.08843/) (2025) - [TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing](https://mlsanity.com/paper/2510.04100/) (2025) - [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026) - [LTM: Large-scale Terrain Model for Wildfire-prone Landscapes](https://mlsanity.com/paper/2607.08711/) (2026) ## Abstract (arXiv) Robust SLAM is a crucial enabler for autonomous navigation in natural, semi-structured 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 RGBD 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 RGBD 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, semi-structured 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.