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. 

@article{kim2024diter,

  title={DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot Navigation in Multi-session Outdoor Environments},

  author={Kim, Juwon,  Kim, Hogyun, Jeong, Seokhwan, Shin, Youngsik,  and Cho, Younggun}

}

Juwon Kim - marimo117@inha.edu (Maintainer)

Hogyun Kim - hg.kim@inha.edu (Sub-Maintainer)

Seokhwan Jeong - eric5709@inha.edu 

Youngsik Shin - yshin86@kimm.re.kr

Younggun Cho - yg.cho@inha.ac.kr