YAML Metadata Warning:The task_categories "point-cloud" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Pico Robotics Dataset · Advanced Edition

Pico + Tracker 机器人学习数据集 — 进阶版

在基础版之上，增加立体点云、21 关节手部、世界坐标系下的位姿对齐数据，并提供 MCAP 格式支持 Foxglove 可视化。适合需要三维空间理解的机器人学习研究。

🔒 本仓库为门禁（Gated）数据集，申请通过后即可下载。

👉 点击这里填写表单，自动获取访问权限。

🎯 三版本对比

版本	内容	访问方式
Basic	去畸变双路视频 + 逐帧深度图 + 双路麦克风音频 + ~1kHz 6DoF 头部位姿 + 相机标定	🟢 公开下载
Advanced（本仓库）	基础版全部 + 位姿对齐立体点云(.npz) + 21 关节手部 + 世界坐标点云 + MCAP/Foxglove 可视化	🔒 申请访问
Annotated	进阶版全部 + 动作分段粗标 + segments.json 标注文件	🔒 申请访问

📁 目录结构

sample_01/
├── head_left_camera_undistorted.mp4    # 左摄像头去畸变视频
├── head_right_camera_undistorted.mp4   # 右摄像头去畸变视频
├── undistort_camera.json               # 相机标定参数
├── video_index.json                    # 帧索引 + 6DoF 位姿
├── depth/                              # 逐帧深度图
├── point_cloud/                        # 立体点云 (.npz)
│   ├── frame_000001.npz
│   └── ...
├── hand_landmarks/                     # 21 关节手部关键点
│   ├── frame_000001.json
│   └── ...
├── world_pose/                         # 世界坐标位姿对齐
└── recording.mcap                      # MCAP 格式，Foxglove 可直接播放

🚀 点云快速预览

import numpy as np

data = np.load("sample_01/point_cloud/frame_000001.npz")
points = data["points"]    # (N, 3) xyz
colors = data["colors"]    # (N, 3) rgb
print(f"点数量: {len(points)}")

🦊 Foxglove 可视化

直接用 Foxglove Studio 打开 recording.mcap：

下载 Foxglove Studio
拖入 .mcap 文件
即可查看点云、位姿、图像、手部关键点等所有数据

📜 许可证

本数据集采用 CC BY 4.0 协议发布。使用时请注明：

Pico Robotics Dataset by skycn110, licensed under CC BY 4.0

📬 联系与合作

数据咨询 / 商业合作：skycn110@gmail.com
申请粗标版：填写表单

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