Datasets:

arthurpompeu
/

lecrop-data

license: other
task_categories:
  - robotics
tags:
  - robotics
  - agriculture
  - under-canopy
  - navigation
  - rosbag
  - terrasentia
pretty_name: LeCropFollow
configs:
  - config_name: cpp
    data_files: cpp.parquet
  - config_name: crow
    data_files: crow.parquet
  - config_name: lecrop
    data_files: lecrop.parquet

LeCropFollow

Latent Space Planning for Navigation in Unstructured Crop Fields

Felipe Tommaselli¹ · Francisco Affonso² · Arthur Rocha¹ · Gianluca Capezzuto¹
Arun Narenthiran Sivakumar² · Girish Chowdhary² · Marcelo Becker¹

¹ University of Sao Paulo ² University of Illinois Urbana-Champaign

IEEE Robotics and Automation Letters, 2026

Models

Please check the Files and versions for our most up-to-date models. For more information, check: https://github.com/Felipe-Tommaselli/lecropfollow

Data

Navigation datasets from a TerraSentia agricultural robot driving under the canopy, extracted from ROS1 bags. Three sources/controllers:

Config	Episodes	Description
CropFollow++ (`cpp`)	143	Crop-follow / pure-pursuit (logs `path`)
CROW (`crow`)	31	iLQR controller (logs `crop_lines`, `goal`, `ilqr_time`)
LeCropFollow (`lecrop`)	137	MPPI/RL + vision (logs `dist_err`, `head_err`, `mppi_*`, `keypoint`)

from datasets import load_dataset
ds = load_dataset("arthurpompeu/lecrop-data", "cpp", split="train")
ep = ds[0]
# ep["rgb"]            -> per-episode video (camera)
# ep["odom_pos_x"], ep["odom_vel_x"], ep["cmd_lin_x"], ...  -> time series

Structure

Each row = one episode: a stretch where the robot drove through the field until it stopped for a while. Episodes were segmented from the (smoothed) odom speed: "moving" when v > 0.05 m/s; a new episode is cut when it stays stopped for >= 5 s. A single bag can yield several episodes; episodes shorter than 2 s or with < 20 messages were dropped. Bags without odometry become a single episode (the whole bag).
Each column = one signal (a ROS topic), stored as a list (the time series for that episode). Topics have different rates, so each one has its own time vector *_t (seconds, relative to the episode start) and its own length.
Videos (rgb, lidar_plot, keypoint_vis_*) are the Video type (MP4/H.264): one video per episode at ~10 fps, with per-frame times in *_t. The HF viewer renders a player.
Removed: rosbag-level fields (header, seq, stamp, frame_id, covariance, layout), heavy raw sensors (depth and LiDAR/PointCloud) and plumbing (tf, camera_info).

Note — browsable version. This published version is downscaled for the dataset viewer: videos are re-encoded to 320×180 and each numeric signal is sub-sampled to <= 250 samples per episode. This keeps trends/shapes intact and makes the viewer fast, but it is not full resolution. For training, request the full-rate / 640×360 variant.

Columns

Metadata (scalars): source, episode, bag, duration_s, n_msgs.

Signals (lists; <g>_t = relative time in s for group <g>):

Group	Columns	Source
`odom_*`	`odom_t`, `odom_pos_{x,y,z}`, `odom_quat_{x,y,z,w}`, `odom_vel_{x,y,z}`, `odom_angvel_{x,y,z}`	`/…/dlio/odom_node/odom`
`imu_*`	`imu_t`, `imu_acc_{x,y,z}`, `imu_gyro_{x,y,z}`, `imu_quat_{x,y,z,w}`	`/…/imu`
`cmd_*`	`cmd_t`, `cmd_lin_{x,y,z}`, `cmd_ang_{x,y,z}`	`/…/cmd_vel`
`motion_*`	`motion_t`, `motion_lin_{x,y,z}`, `motion_ang_{x,y,z}`	`/…/motion_command`
`path_*`	`path_t`, `path_pos_{x,y,z}`, `path_quat_{x,y,z,w}` (list of lists: a polyline per step)	`/…/path`
`goal_*`	`goal_t`, `goal_pos_{x,y,z}`, `goal_quat_{x,y,z,w}`	`/…/goal` (crow)
`crop_lines_*`	`crop_lines_t`, `crop_lines_{m1,b1,m2,b2}` (crop-row lines)	`/…/crop_lines` (crow)
`ilqr_*`	`ilqr_t`, `ilqr_time`	`/…/ilqr_time` (crow)
`dist_err`, `head_err`	predicted lateral / heading error	`/…/*_error_predicted` (lecrop)
`mppi_dist`, `elite_scores`, `value_info`	MPPI/RL debug (lists of lists)	`/…/rl_debug/*` (lecrop)
`keypoint`	vision keypoints (list of lists)	`/…/vision/keypoint` (lecrop)

Videos (Video, MP4/H.264, ~10 fps; <g>_t = per-frame time):

Column	Content	Source
`rgb`	RGB camera video	`/…/rgb/image_rect_color/compressed` (all)
`lidar_plot`	LiDAR plot with crop rows	`/lidar_plot` (crow)
`keypoint_vis_argmax`, `keypoint_vis_heatmap`	keypoint-visualization videos	`/…/vision/keypoint_vis_*/compressed` (lecrop)

Source-specific columns are null when an episode does not have them (e.g. some cropfollowpp_lecropfollow bags inside cpp carry lecrop columns).

Citation

Please, consider citing our work:

@ARTICLE{tommaselli2026lecropfollow,
  author={Tommaselli, Felipe and Affonso, Francisco and Rocha, Arthur and Capezzuto, Gianluca and Sivakumar, Arun Narenthiran and Chowdhary, Girish and Becker, Marcelo},
  journal={IEEE Robotics and Automation Letters}, 
  title={LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields}, 
  year={2026},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/LRA.2026.3710052}
}

License

Code is released under the MIT License. The paper is published under CC BY.