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

TL;DR

This paper introduces the DLR S3LI dataset, recorded in challenging volcanic environments, to evaluate and improve SLAM systems under conditions similar to lunar or Martian terrains, emphasizing sensor complementarity.

Contribution

The paper provides a novel dataset capturing extreme environmental conditions to test SLAM systems and encourages development of approaches leveraging both visual and LiDAR data.

Findings

Highlights limitations of current SLAM in unstructured environments

Demonstrates challenges posed by visual aliasing and limited LiDAR FOV

Provides a benchmark for future SLAM research in planetary-like terrains

Abstract

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