Datasets:
File size: 1,770 Bytes
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pretty_name: "SanD-Planner — dataset_avoid (obstacle-avoidance subset)"
tags:
- robotics
- navigation
- trajectory-prediction
- depth
- diffusion-policy
---
# SanD-Planner training data — `dataset_avoid`
The **`dataset_avoid`** subset (obstacle-avoidance runs) used to train the
[**SanD-Planner**](https://github.com/WangJinCheng1998/sandplanner) local trajectory
planner.
- Collected in a **custom Gazebo simulation environment** (self-built).
- This is **one subset of a larger training set** — only part of the full dataset is released here.
## Contents
A single archive `dataset_avoid.zip` (~14 GB) that extracts to:
```
dataset_avoid/
└── run_XXXX/ # 152 runs, variable length
├── traj_xyz.npy # (T, 3) trajectory positions (x, y, z), metres
├── traj_yaw.npy # (T,) heading (yaw)
├── traj_pitch.npy # (T,) pitch
└── depth/
└── depth_NNNN.png # per-frame depth images (uint16, mm)
```
## Usage
```bash
hf download WJCUCL/sandplanner-dataset-avoid dataset_avoid.zip --repo-type dataset --local-dir .
unzip dataset_avoid.zip -d dataset/ # -> dataset/dataset_avoid/run_XXXX/
DATASET_ROOT=dataset bash run_train.sh # run inside the SanD-Planner repo
```
See the [SanD-Planner repository](https://github.com/WangJinCheng1998/sandplanner) for
the training and inference code.
## Citation
```bibtex
@article{wang2026sand,
title = {SanD-Planner: Sample-Efficient Diffusion Planner in B-Spline Space for Robust Local Navigation},
author = {Wang, Jincheng and Bao, Lingfan and Yang, Tong and Plasencia, Diego Martinez and Jiao, Jianhao and Kanoulas, Dimitrios},
journal = {arXiv preprint arXiv:2602.00923},
year = {2026}
}
```
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