Datasets:

ROVR-Network
/

ROVR-Open-Dataset

The ROVR Open Dataset provides processed data saved only in ROS 2 bag format, suitable for autonomous driving, robotics, and 4D perception tasks. This document outlines the directory structure and field specifications of the dataset.

Data Directory Structure

ROVR-Open-Dataset/
├── Samples/                         # Sample data clips
│   ├── ${bag_name}/                 # Folder name matches ROS bag name
│   │   ├── images/                  # Image data (.png)
│   │   ├── pointclouds/             # Point cloud data (.pcd)
│   │   ├── depth/                   # Depth image (.png)
│   │   ├── annotation/              # Point cloud detection and segmentation results
│   │   │   ├── detection_result/    # Detection results (.txt)
│   │   │   └── segmentation_result/ # Segmentation results (.txt)
│   │   ├── ego_poses.json           # Interpolated GNSS positioning data
│   │   ├── ego_poses_raw.json       # Raw GNSS positioning data
│   │   └── imu_data.csv             # IMU data (CSV format)
├── ROVR_intrinsics_extrinsics/      # Camera and LiDAR parameter files
│   ├── ${device_serial}/            # Subfolder named by device serial number (e.g., 1025040009)
│   │   ├── ext.yaml                 # LiDAR-to-camera extrinsic parameters
│   │   └── int.yaml                 # Camera intrinsic parameters

Detailed Structure Description

Samples/${bag_name}/

Description: Contains processed data clips with folder names matching the ROS bag names (e.g., 20250517173254-1025040009-34-lUNe).
Subfolders and files:
- images/: PNG images (1920 x 1080 px).
- pointclouds/: Point cloud data in PCD format.
- depth/: PNG images (1920 x 1080 px).
- annotation/: Point cloud detection and segmentation results, containing:
  - detection_result/: Detection results in .txt format.
  - segmentation_result/: Segmentation results in .txt format.
- ego_poses.json: Interpolated GNSS positioning data.
- ego_poses_raw.json: Raw GNSS positioning data.
- imu_data.csv: IMU data in CSV format.

ROVR_intrinsics_extrinsics/${device_serial}/

Description: Camera and LiDAR parameters corresponding to the device serial number.
Files:
- ext.yaml: LiDAR-to-camera extrinsic parameters (rotation matrix and translation vector).
  - Note: Apply coordinate transform before use: x = -y, y = -z, z = x.
- int.yaml: Camera intrinsic parameters (focal length, principal point, distortion coefficients).

Naming Conventions

Folder Naming Format

Format: <CollectionTime>-<DeviceSerialNumber>-<CollectionSequenceNumber>-<IdentifierCode>
Example: 20250517173254-1025040009-34-lUNe
Field Explanation:
- Collection Time: YYYYMMDDhhmmss (e.g., May 17, 2025, 17:32:54), in Greenwich Mean Time (UTC).
- Device Serial Number: Unique device identifier (e.g., 1025040009).
- Collection Sequence Number: Sequence number within the collection (e.g., 34).
- Identifier Code: Data verification identifier (e.g., lUNe).

Sensor Data in `Samples/${bag_name}/`

File/Folder	Description	Format	Frame Rate	Coordinate System Definition
images/	Image data	.png	5 Hz	X: Right, Y: Down, Z: Forward
pointclouds/	Point cloud data	.pcd	5 Hz	X: Forward, Y: Left, Z: Up
depth/	Depth image data	.png	5 Hz	X: Right, Y: Down, Z: Forward
annotation/	Detection and segmentation results	.txt	5 Hz
ego_poses.json	Interpolated GNSS positioning data	.json	5 Hz	According to GNSS standard
ego_poses_raw.json	Raw GNSS positioning data	.json	1 Hz	According to GNSS standard
imu_data.csv	IMU data	.csv	100 Hz	X: Backward, Y: Left, Z: Down

Camera and LiDAR Parameters in `ROVR_intrinsics_extrinsics/${device_serial}/`

File/Folder	Description	Format
ext.yaml	LiDAR-to-camera extrinsic parameters, including rotation vector and translation vector.	.yaml
int.yaml	Camera intrinsic parameters, including focal length, principal point, and distortion coefficients.	.yaml

Descriptions:

Sensor Data in `Samples/${bag_name}/`

1. `images/`

Description: PNG format images with a resolution of 1920×1080 pixels.
File Naming Convention:
The image filenames are based on UTC timestamps, precise to nanoseconds, ensuring that the file names correspond directly with other data files (point cloud, annotation).

2. `pointclouds/`

Description: PCD format point cloud data.
File Naming Convention:
The point cloud filenames follow the same UTC timestamp convention as the image filenames, ensuring that each point cloud file corresponds to a specific image file.

3. `depth/`

Description: Depth images in single-channel PNG format with a resolution of 1920×1080 pixels, generated by projecting LiDAR point cloud data onto the image plane using the camera’s intrinsic and distortion parameters, along with the extrinsic calibration between the camera and LiDAR. Each pixel value represents depth (i.e., distance from the camera) in millimeters. A pixel value of 0 indicates that no LiDAR point was projected onto that pixel (i.e., no valid depth information is available at that location).
File Naming Convention:
The depth image filenames follow the same UTC timestamp convention as the point cloud filenames, ensuring that each depth image file corresponds to a specific point cloud file.

4. `annotation/`

Description: Detection and segmentation results for the images and point cloud.
File Naming Convention:
The annotation filenames are also based on the same UTC timestamp as the corresponding image and point cloud files. This ensures that the annotations match the exact frame captured in the image and point cloud data.

Object Detection Annotations (`detection_result/`)

Description: 3D and 2D object detection results in plain text format (.txt), each line representing a detected object with detailed fields. Each line contains:

[category_id] [tracking_id] [alpha] [x1] [y1] [x2] [y2] [height] [width] [length] [x] [y] [z] [rotation_y] [bbox_3d_points...]

Explanation:

category_id: Object class ID:
- 1: Motor_vehicle, 2: Pedestrian, 3: Non-motor_vehicle, 4: Traffic_light, 5: Traffic_sign, 6: Lane_line, 7: Pole, 8: Traffic_cone, 9: Other, 10: Ground_marking, 11: Road.
tracking_id: Unique ID for tracking the object across frames.
alpha: Observation angle in radians, range [0, 2π].
x1, y1, x2, y2: Top-left and bottom-right corners of the 2D bounding box (pixels). -1 for missing data.
height, width, length: 3D size of the object (in meters).
x, y, z: 3D location in camera coordinates (in meters).
rotation_y: Rotation around Y-axis (radians), range [-π, π].
bbox_3d_points: 8-point 3D bounding box, may be projected to 2D pixel space.

Object Segmentation Annotations (`segmentation_result/`)

Description: 2D image segmentation masks with category and pixel coordinates per object. Each line contains:

[category_id] [object_id] [x1] [y1] [x2] [y2] ... [xn] [yn]

Explanation:

category_id: Same ID system as detection annotations.
object_id: Unique ID per segmented object.
[x1, y1], [x2, y2], ...: Polygon points of the mask (in pixels).

Notes on Annotation Data

Coordinate Systems:
- 3D positions in detection results use camera coordinates. Apply the coordinate transformation from ext.yaml to align with LiDAR.
- Some 3D bounding box fields may be 2D projected points — verify with dataset version.
- Segmentation mask points are in pixel space, aligning with ../image/ data.

5. `ego_poses_raw.json` and `ego_poses.json`

Description:
ego_poses_raw.json: Contains raw GNSS positioning data, which follows the GNSS standard with the following fields:
- Timestamp: GNSS timestamp.
- lat: Latitude.
- lon: Longitude.
- utm_x, utm_y, utm_z: UTM coordinates.
- heading: Heading in degrees (0~360, clockwise from north).
- speed: Speed in m/s.
- date: Date in ddmmyy format.
- hemisphere_ns: Northern or Southern hemisphere.
- hemisphere_ew: Eastern or Western hemisphere.
- quaternion: Quaternion representing the rotation.
ego_poses.json: Contains interpolated GNSS positioning data based on image timestamp, following the same structure as ego_poses_raw.json, but with timestamps matching the image data.

Field	Description
timestamp	GNSS timestamp
lat	Latitude
lon	Longitude
utm_x	UTM X coordinate
utm_y	UTM Y coordinate
utm_z	UTM Z coordinate
heading	Heading (0~360 degrees, clockwise from north)
speed	Speed (in m/s)
date	Date (ddmmyy format)
hemisphere_ns	Northern or Southern Hemisphere
hemisphere_ew	Eastern or Western Hemisphere
quaternion	Quaternion (rotation representation)

Data Example:

{
  "timestamp": 1747503144.1424189,
  "lat": 37.77150665166667,
  "lon": -122.42305399166666,
  "utm_x": 550811.2977794447,
  "utm_y": 4180620.4009261196,
  "utm_z": 0.0,
  "heading": 332.79,
  "speed": 0.0,
  "date": "170525",
  "hemisphere_ns": "N",
  "hemisphere_ew": "W",
  "quaternion": [-0.9719404768572004, 0.0, 0.0, 0.23522693180543294]
}

Explanation:
- ego_poses_raw.json contains raw GNSS positioning data, with a frequency of 1 Hz.
- ego_poses.json contains interpolated GNSS positioning data based on image timestamps, with a frequency of 5 Hz.

6. `imu_data.csv`

Description: IMU raw data in CSV format with columns: [timestamp, acc_x, acc_y, acc_z, gyro_x, gyro_y, gyro_z].

Data Example:

timestamp, acc_x, acc_y, acc_z, gyro_x, gyro_y, gyro_z
1747503144.066422725,-0.06345245393458754,1.0756415122747423,9.818998864889146,6.807146783081999e-06,-0.0035436609232569302,0.0026104203575969863
1747503144.076391309,-0.055494749746285384,1.0912267539650202,9.795408273339271,0.0006594161759019893,-0.003767680104459781,0.0024891367766331452
1747503144.086524977,-0.07948344075120986,1.0916746506839992,9.802085208892823,0.0007679316679443117,-0.003132297461968387,0.0022016243500468475

Explanation:
- Timestamp: UTC Timestamp of the IMU data.
- acc_x, acc_y, acc_z: Acceleration data in the X, Y, Z axes (in m/s²).
- gyro_x, gyro_y, gyro_z: Gyroscope data in the X, Y, Z axes (in rad/s).
- Frequency: 100 Hz.

Camera and LiDAR Parameters in `ROVR_intrinsics_extrinsics/${device_serial}/`

1. `ext.yaml` - LiDAR-Camera Extrinsic Parameters

Description: Contains the extrinsic parameters that define the transformation from the LiDAR coordinate system to the camera coordinate system, using a rotation vector and a translation vector. The filename ext.yaml is fixed, with parameters specific to the device serial number.

Data Example:

# x = -y  # y = -z  # z = x
lidar_to_camera:
  rvec: [1.7649999999999999, 1.008, 0.25209999999999999]  # Rotation vector. Rodrigues. [deg, deg, deg]
  tvec: [-0.016810000000000002, 0, 0.016810000000000002]  # Translation vector. [m, m, m]

Explanation:
- rvec (Rotation Vector): A 3D rotation vector using Rodrigues' rotation formula, given in degrees, describing the rotation between the LiDAR and camera coordinate systems.
- tvec (Translation Vector): A 3D vector describing the translation (offset) between the LiDAR and camera coordinate systems in meters.
- Transformation Matrix: The full extrinsic transformation is represented as a 4x4 homogeneous matrix.

Note: Before applying extrinsic parameters, the LiDAR coordinate system must be transformed using the following rule:

x = -y
y = -z
z = x

2. `int.yaml` - Camera Intrinsic Parameters

Description: Contains the intrinsic parameters of the camera, including focal length, principal point, and distortion coefficients, which define the internal geometry of the camera. The filename int.yaml is fixed, with parameters specific to the device serial number.

Data Example:

FX: 1190.9380383925
FY: 1190.8862851737
CX: 955.6705012175
CY: 540.1090098440
K1: -0.0586809591
K2: -0.4292077180
P1: -0.0000209962
P2: 0.0000513478
K3: -0.0282192110
K4: 0.3687679523
K5: -0.5661097302
K6: -0.1486583365
RMS: 0.0095

Explanation:
- FX, FY: Focal lengths in the X and Y axes (in pixels).
- CX, CY: Principal point (optical center) in the X and Y axes (in pixels).
- K1, K2, K3, K4, K5, K6: Radial distortion coefficients, modeling lens distortion.
- P1, P2: Tangential distortion coefficients, modeling distortion due to lens misalignment.
- RMS: Root mean square error of the calibration process.

Usage

Data Access: Find the processed data in the Samples/${bag_name}/ folder, and the corresponding camera and LiDAR parameters in the ROVR_intrinsics_extrinsics/${device_serial}/ folder.
Repository: https://github.com/rovr-network/ROVR-Open-Dataset

This repository currently contains only one sample ROS bag.
Access Full Dataset: Google Drive

Contact

For inquiries or commercial licensing, please contact: [email protected].

Data Directory Structure and Field Specifications

Overview

Data Directory Structure

Detailed Structure Description

Naming Conventions

Folder Naming Format

Sensor Data in Samples/${bag_name}/

Camera and LiDAR Parameters in ROVR_intrinsics_extrinsics/${device_serial}/

Descriptions:

Sensor Data in Samples/${bag_name}/

1. images/

2. pointclouds/

3. depth/

4. annotation/

Object Detection Annotations (detection_result/)

Explanation:

Object Segmentation Annotations (segmentation_result/)

Explanation:

Notes on Annotation Data

5. ego_poses_raw.json and ego_poses.json

Data Example:

6. imu_data.csv

Data Example:

Camera and LiDAR Parameters in ROVR_intrinsics_extrinsics/${device_serial}/

1. ext.yaml - LiDAR-Camera Extrinsic Parameters

Data Example:

2. int.yaml - Camera Intrinsic Parameters

Data Example:

Usage

Contact

Sensor Data in `Samples/${bag_name}/`

Camera and LiDAR Parameters in `ROVR_intrinsics_extrinsics/${device_serial}/`

Sensor Data in `Samples/${bag_name}/`

1. `images/`

2. `pointclouds/`

3. `depth/`

4. `annotation/`

Object Detection Annotations (`detection_result/`)

Object Segmentation Annotations (`segmentation_result/`)

5. `ego_poses_raw.json` and `ego_poses.json`

6. `imu_data.csv`

Camera and LiDAR Parameters in `ROVR_intrinsics_extrinsics/${device_serial}/`

1. `ext.yaml` - LiDAR-Camera Extrinsic Parameters

2. `int.yaml` - Camera Intrinsic Parameters