# Challenges of SLAM in extremely unstructured environments: the DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

arXiv: https://arxiv.org/abs/2207.06815 · HTML: https://mlsanity.com/paper/2207.06815/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2022.

## Built on by (most cited first, 9 of 9)

- [Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments](https://mlsanity.com/paper/2403.13395/) (2024)
- [LuSNAR:A Lunar Segmentation, Navigation and Reconstruction Dataset based on Muti-sensor for Autonomous Exploration](https://mlsanity.com/paper/2407.06512/) (2024)
- [Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments](https://mlsanity.com/paper/2511.05404/) (2025)
- [Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration](https://mlsanity.com/paper/2304.14520/) (2023)
- [The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments](https://mlsanity.com/paper/2601.19557/) (2026)
- [LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation](https://mlsanity.com/paper/2604.09352/) (2026)
- [Visual Relocalization from Sparse Views in Aliased and Low-Texture Environments via Novel View Synthesis](https://mlsanity.com/paper/2607.22147/) (2026)
- [MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain](https://mlsanity.com/paper/2410.05182/) (2024)
- [Visual SLAM with DEM Anchoring for Lunar Surface Navigation](https://mlsanity.com/paper/2603.17229/) (2026)

## Abstract (arXiv)

We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a hand-held sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utilized Solid-State LiDAR sensor, challenges traditional LiDAR SLAM for the task of pose estimation using point clouds alone. With this data, that covers more than 4 kilometers of travel on soft volcanic slopes, we aim to: 1) provide a tool to expose limitations of state-of-the-art SLAM systems with respect to environments, which are not present in widely available datasets and 2) motivate the development of novel localization and mapping approaches, that rely efficiently on the complementary capabilities of the two sensors. The dataset is accessible at the following url: https://rmc.dlr.de/s3li_dataset