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https://github.com/jrcuaranv/terrasentia-dataset

This dataset is intended for the evaluation of visual-based localization and mapping systems in agriculture.
https://github.com/jrcuaranv/terrasentia-dataset

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This dataset is intended for the evaluation of visual-based localization and mapping systems in agriculture.

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README 

        
# Terrasentia-dataset

[Download](https://uofi.box.com/s/l7e0okjxkq1vzz77goac2bu1my81ad9d)



[Paper](https://journals.sagepub.com/doi/abs/10.1177/02783649231215372)



[Paper - Supplementary material](https://uofi.box.com/s/cns333ty3t04lib5mjnq16tycxokztp2)



This dataset is intended for the evaluation of visual-based localization and mapping systems in agriculture. It includes stereo

images, IMU, GPS, and wheel encoder measurements. It was collected from a ground robot in the Illinois Autonomous Farm at the

University of Illinois at Urbana-Champaign. The collection campaign took place during the Summer of 2022. Different data sequences

were collected twice per week in corn fields, and less often in soybean and sorghum. This dataset exhibit high variability in terms of

weather conditions and growth stages. It contains challenging features like occlusions, illumination variations, weeds, dynamic objects, and rough terrain.





  

    Terrasentia-robot

  




## Data description

This dataset is organized in different folders, classified by crop type and collection date (See Table I for further description of each folder).

Within each folder, we find Rosbag (*.bag) and SVO (*.svo) files.

- The Rosbag files comprise standard ROS messages for all the sensor measurements, including the stereo-inertial camera (832x468 px, 30

fps), IMU, GPS, and wheel encoders. It also includes the estimated ground-truth robot pose (by using an EKF). Finally, it includes additional

topics computed by the ZED software development kit (SDK) that you may find helpful for comparison purposes. Among these topics, we have

a VIO topic, a camera pose topic (estimated by a VI-SLAM system), and two path topics (path-odom and path-map), which correspond to the

history of robot poses computed by the VIO and the VI-SLAM system of the ZED SDK. Table II summarizes the topics contained in each

Rosbag file.

- The SVO files comprise right and left images in H265 (HEVC) mode, achieving a compression ratio of 100:1 while keeping high quality. The

image resolution is 1280x720 px. These files can be played using the ZED ROS Wrapper (see tutorial below), to get the same topics as the camera

was connected. One of the advantages of this file format is its compactness and the possibility to get high-quality depth information by using the

neural mode of the ZED software development kit.



### Table I. Main characteristics of data folders

```

Folder          Number of    Time        Occlusions     Presence of     Weather         Growth-stage       Rough      Folder

                sequences    span                       Weeds           variability     variability        terrain    size (GB)

Cornfield1      80           4 months    ✓              ✓               ✓               ✓                  ✓          584

Cornfield2      17           3 months    ✓              ✓               ✓               ✓                  ✓          171

Cornfield3      2            1 week      x              x               x               x                  ✓          28

Cornfield4      4            1 months    ✓              x               x               ✓                  ✓          37

Sorghum         2            3 weeks     ✓              ✓               x               x                  ✓          9

Soybean         12           2 weeks     ✓              ✓               ✓               x                  ✓          79

Sweet Corn      4            1 weeks     ✓              ✓               x               x                  ✓          49

Others          14           3 months    x              x               ✓               ✓                  ✓          103

```



### Table II. ROS topics in Rosbag files



  

    Rosbag-topics

  




## Playing SVO files (optional)

### Left image and neural depth computed by the ZED SDK





  

    neural-depth

  




If you want higher resolution images and higher depth quality than that of the rosbag files, then you can use the svo files. Most of them

were recorded simultaneously with the rosbags.

1. Install the [ZED SDK](https://www.stereolabs.com/developers/release/) (You need CUDA)

2. install the [ZED ROS Wrapper](https://www.stereolabs.com/docs/ros/)

3. Play the svo files:

```

cd ~/catkin_ws

source devel/setup.bash

roslaunch zed_wrapper zed2.launch svo_file:=/home/path_to_svo_file/file.svo

```

4. See all the ROS topics as the camera was connected

```

rostopic list

```

You can change the quality of the depth image and other parameters by editing the file common.yaml in the directory:

catkin_ws/src/zed-ros-wrapper/zed_wrapper/params. To get the Intrinsic parameters of the camera, check topics:

- /zed2/zed_node/depth/camera_info

- /zed2/zed_node/left/camera_info

- /zed2/zed_node/right/camera_info



## Building fpn_msgs in your catkin workspace

In order to read the GPS and motor messages, which are user-defined ROS messages, you will need to build them in your catkin workspace. [Further details](http://wiki.ros.org/ROS/Tutorials/CreatingMsgAndSrv).

1. Download the package [fpn_msgs](https://uofi.box.com/shared/static/sxfuvw9njpm2e2mbcfncirdxx70kn165.zip) to your catkin/src folder and unzip the file.

2. Go to your catkin workspace and build the package

```

catkin_make fpn_msgs

```

3. Source your workspace

```

source devel/setup.bash

```



## Extracting data from rosbag files

1. Build fpn_msgs as explained above

2. Edit script extract_data_from_rosbag.py, customizing topics to extract and output directory

3. Run script

```

source ~/catkin_ws/devel/setup.bash

python3 extract_data_from_rosbag.py



```



## Sensor calibration

### Robot's coordinate frames



  

    coordinate-frames

  




Download [sensor_parameters.txt](sensor_parameters.txt)