Introduction
The Goat-core dataset provides comprehensive data for downstream target localization in robotics and computer vision. It features 4 distinct scenes (5cd, 4ok, nfv, tee).
The dataset records essential environmental data including Depth maps, RGB images, Local Position (local_pos), and Camera Intrinsics. Within the ground truth data, we define downstream localization tasks organized into episodes and sub-tasks.
4
Scenes
6
Episodes per Scene
20
Tasks per Episode
3
Task Types
File Structure
The dataset is organized into two main directories: dataset (raw sensor data) and groundtruth (task definitions).
Goat-core
├── dataset
│ ├── 4ok
│ ├── 5cd
│ ├── nfv
│ └── tee
│ ├── depth # Depth maps
│ ├── images # RGB images
│ ├── sparse/0/cameras.txt # Intrinsics
│ └── local_pos.txt # Local position data
│
└── groundtruth
├── 4ok
├── 5cd
├── nfv
└── tee
├── 0 # Episode 0
├── 1 # Episode 1
├── 2 # Episode 2
├── 3 # Episode 3
├── 4 # Episode 4
├── 5 # Episode 5
├── 01clothes # Specific Sub-task Folder
│ ├── language.txt # Task Descriptions
│ ├── pos.txt # Position Groundtruth
│ ├── task_type.txt # Task Type Labels
│ ├── 01clothes_0.png # Image Index 0 (Anchor)
│ └── ...
├── 02towel
└── 03bed
Task Indexing Guide
Tasks are categorized into three types, each indexed differently:
- Object Task: Indexed via
language.txt.
- Language Task: Indexed via
language.txt.
- Image Task: Indexed using the image with ID 0 (e.g.,
00rack_0.png).
Access the Data
The dataset is available for academic and research use.
Download Goat-core Dataset
Hosted on [Google Drive / Hugging Face] | License: CC BY-NC 4.0
Citation
If you use Goat-core in your research, please cite the following paper:
@misc{
zhou2025lagmemo,
title={LagMemo: Language 3D Gaussian Splatting Memory for Multi-modal Open-vocabulary Multi-goal Visual Navigation},
author={Zhou, Haotian and Wang, Xiaole and Li, He and Sun, Fusheng and Guo, Shengyu and Qi, Guolei and Xu, Jianghuan and Zhao, Huijing},
journal={arXiv preprint arXiv:2510.24118},
year={2025}
howpublished={\url{https://weekgoodday.github.io/lagmemo/}},
}