Datasets:

Seba-san
/

AltoValleDataset

@@ -4,6 +4,8 @@ task_categories:
 - object-detection
 - image-segmentation
 - robotics
 language:
 - en
 - es
@@ -12,59 +14,113 @@ tags:
 - robotics
 - orchards
 - alto-valle
 pretty_name: Alto Valle Dataset
 size_categories:
-- 10G<n<100G
 ---
 # Alto Valle Dataset
-This database is still under full development. Any contribution is welcome.
-## Legal Notice
-The content of this database is under **Copyright license** of the **Universidad Nacional del Comahue** and **INTA EEAV**.
-## References and Publications
-This work was presented at the **Jornadas Argentinas de Robótica 2022**.
-*   **Paper:** [Read PDF](https://github.com/Seba-san/AltoValleDataset/blob/main/AVD_v0.pdf)
-*   **Presentation:** [View Slides](https://github.com/Seba-san/AltoValleDataset/blob/main/presentacion.pdf)
-*   **Video Exhibition:**
-[![Video Exhibition](https://github.com/Seba-san/AltoValleDataset/blob/main/gifs/fake_video.png)](https://youtu.be/qrSIFyLzFrQ)
-## Dataset Description
-The **Alto Valle Dataset** focuses on agricultural environments, specifically orchards in the Alto Valle region (Argentina). It allows researchers to test algorithms for robotics in agriculture, such as localization, mapping, and crop monitoring.
-The dataset includes data captured in different seasons (April and August) and from different perspectives (Ground and Aerial).
-### How to get the data
-You can download the formatted data directly from the files in this repository (if uploaded here) or from the original server:
-*   **Original Server Link:** [DOWNLOAD HERE](http://190.124.230.117/AVD/).
-> **Note:** Currently, the original file server has some instability. If you have problems downloading the data, please open a thread in the "Community" tab (or Issues in GitHub) to report the problem. We compiled some solutions that worked in the past in this [LINK](https://github.com/Seba-san/AltoValleDataset/blob/main/descarga.md).
-## Data Examples
-### April Data Examples (Autumn)
-![](https://github.com/Seba-san/AltoValleDataset/blob/main/gifs/abril.gif)
-### August Data Examples (Winter/Pruning season)
-![](https://github.com/Seba-san/AltoValleDataset/blob/main/gifs/agosto.gif)
-### Aerial View
-![](https://github.com/Seba-san/AltoValleDataset/blob/main/gifs/aereo.gif)
-## Citation
-If you use this dataset in your research, please cite the work presented at JAR 2022:
 ```bibtex
 @inproceedings{alto_valle_dataset_2022,
-  title={Alto Valle Dataset},
-  author={Universidad Nacional del Comahue and INTA EEAV},
-  booktitle={Jornadas Argentinas de Robótica},
   year={2022},
   url={https://github.com/Seba-san/AltoValleDataset}
-}

 - object-detection
 - image-segmentation
 - robotics
+- slam
+- navigation
 language:
 - en
 - es
 - robotics
 - orchards
 - alto-valle
+- stereo-vision
+- lidar
+- gnss-rtk
 pretty_name: Alto Valle Dataset
 size_categories:
+- 100G<n<1T
+viewer: false
 ---
 # Alto Valle Dataset
+<div align="center">
+  <img src="https://raw.githubusercontent.com/Seba-san/AltoValleDataset/main/gifs/abril.gif" width="400"/>
+  <p><em>Autonomous navigation data in Alto Valle Pear Orchards</em></p>
+</div>
+## 📖 Overview
+The **Alto Valle Dataset** is a collection of experimental data acquired in pear orchards (*Pyrus communis*) at the **INTA Alto Valle Experimental Station** (Río Negro, Argentina).
+The main goal of this dataset is to support the development of **localization, mapping (SLAM), and navigation algorithms** in agricultural environments. It presents unique challenges such as:
+*   High variability in lighting and terrain.
+*   Seasonal changes: Data captures during **Summer/Autumn** (leafy, pre-harvest) and **Winter** (leafless, pruning).
+*   Repetitive structures (rows of trees).
+## 🚜 Hardware & Setup
+The data was captured using a modified **N. Blosi Senior** harvesting platform moving at approximately $0.3 m/s$.
+| Sensor Type | Model | Specifications |
+| :--- | :--- | :--- |
+| **Stereo Camera** | **Stereolabs ZED** | 720x1280 @ 15 FPS. USB 3.0 connected to Nvidia Jetson TX1. |
+| **LiDAR** | **SICK LMS-100** | 2D LiDAR, 50Hz (stored at 1Hz), 270° FOV, 0.5° resolution. |
+| **GNSS-RTK** | **U-BLOX C94-M8P-2** | Base + Rover setup (915MHz link). ~2.5cm accuracy. 1Hz update rate. |
+The sensors were mounted 2.5m high, with the camera acting as the reference frame.
+## 📁 Dataset Structure
+The dataset is organized by sequences (April and August). Each sequence contains:
+```text
+dataset/
+├── sequenceXX/
+│   ├── images/
+│   │   ├── left_<index>.png    # Rectified left image
+│   │   ├── right_<index>.png   # Rectified right image
+│   │   └── timestamps.txt      # Image timestamps
+│   ├── lidar.csv               # LiDAR readings
+│   └── gnss.csv                # GNSS-RTK readings
+```
+### Data Format Details
+*   **Images:** PNG format, rectified.
+*   **GNSS (`gnss.csv`):**
+    Format: `[latitude | longitude | timestamp]`
+*   **LiDAR (`lidar.csv`):**
+    Format: `[timestamp | nscan | 541 x range]`
+    *   `nscan`: Frame number generated by the sensor.
+    *   `range`: 541 distance values (0.5° angular resolution).
+## 📐 Calibration Parameters
+Intrinsic parameters for the **ZED Camera** used in this dataset. The baseline is **120.647 mm**.
+| Parameter | Left Camera | Right Camera |
+| :--- | :--- | :--- |
+| **fx** | 692.964 | 698.848 |
+| **fy** | 692.964 | 698.848 |
+| **cx** | 576.186 | 737.995 |
+| **cy** | 367.798 | 361.795 |
+| **k1** | -0.182798 | -0.1634 |
+| **k2** | 0.0277213 | 0.0214219 |
+*Extrinsic parameters (transformations between sensors) are available in the PDF documentation or the paper.*
+## 📅 Seasons & Conditions
+The dataset covers different phenological stages of the pear crops (Williams, Abate Fetel, and Beurré D’Anjou varieties):
+1.  **April 09, 2018 (Autumn):**
+    *   **Condition:** Pre-harvest. Dense foliage, branches weighed down by fruit.
+    *   **Weather:** Sunny, variable lighting (shadows/direct sun).
+    *   **Ground:** Presence of weeds, irrigation ditches.
+2.  **August 06, 2018 (Winter):**
+    *   **Condition:** Pruning season. Deciduous trees (no leaves). Visible trunks and structure.
+    *   **Weather:** Partly cloudy/overcast.
+    *   **Ground:** Cleared weeds.
+## 📥 Access & Download
+The data is split into `.tar` archives due to size. You can download them directly from the **[Files and versions](https://huggingface.co/datasets/Seba-san/AltoValleDataset/tree/main)** tab.
+## ⚖️ Legal Notice & Citation
+The content of this database is under **Copyright** of the **Universidad Nacional del Comahue** and **INTA EEAV**.
+If you use this dataset in your research, please cite the work presented at **Jornadas Argentinas de Robótica (JAR) 2022**:
+*   **Paper:** [Read PDF (Spanish)](https://github.com/Seba-san/AltoValleDataset/blob/main/AVD_v0.pdf)
+*   **Video:** [Watch on YouTube](https://youtu.be/qrSIFyLzFrQ)
 ```bibtex
 @inproceedings{alto_valle_dataset_2022,
+  title={Alto Valle Dataset: colección de datos experimentales enfocados en el estudio y desarrollo de algoritmos de navegación mediante visión en ambientes frutícolas},
+  author={Sansoni, Sebastian and Raverta Capua, Francisco and Moreyra, Marcelo L. and Benitez Piccini, Edgardo},
+  booktitle={Jornadas Argentinas de Robótica (JAR)},
   year={2022},
+  organization={Universidad Nacional del Comahue & INTA EEAV},
   url={https://github.com/Seba-san/AltoValleDataset}
+}
+```
+## Acknowledgments
+This work was supported by the Project of Social Technological Development (PDTS) "Sistemas de Asistencia al Productor y Automatización de Máquinas para la Fruticultura de la Norpatagonia" (PDTS404), funded by CIN and CONICET.
+```