# DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot SLAM in Multi-session Environments

arXiv: https://arxiv.org/abs/2412.05839 · HTML: https://mlsanity.com/paper/2412.05839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments](https://mlsanity.com/paper/2601.19557/) (2026)

## Abstract (arXiv)

We encounter large-scale environments where both structured and unstructured spaces coexist, such as on campuses. In this environment, lighting conditions and dynamic objects change constantly. To tackle the challenges of large-scale mapping under such conditions, we introduce DiTer++, a diverse terrain and multi-modal dataset designed for multi-robot SLAM in multi-session environments. According to our datasets' scenarios, Agent-A and Agent-B scan the area designated for efficient large-scale mapping day and night, respectively. Also, we utilize legged robots for terrain-agnostic traversing. To generate the ground-truth of each robot, we first build the survey-grade prior map. Then, we remove the dynamic objects and outliers from the prior map and extract the trajectory through scan-to-map matching. Our dataset and supplement materials are available at https://sites.google.com/view/diter-plusplus/.