ITLCampus-SM

The dataset was recorded on the Husky robotics platform on the university campus and consists of 5 tracks recorded at different times of day (day/dusk/night) and different seasons (winter/spring).

Data

Track	Season	Time of day	Frames, pcs	Front cam, res	Back cam, res	LiDAR, rays	6 DoF pose	Semantic masks
00_2023-02-21	winter	day	$620$	$1920\times 1080$	$1920\times 1080$	16	☑	front + back $1920\times 1080 \times 65$ classes
01_2023-03-15	winter	night	$626$	$1920\times 1080$	$1920\times 1080$	16	☑	front + back $1920\times 1080 \times 65$ classes
02_2023-02-10	winter	twilight	$609$	$1920\times 1080$	$1920\times 1080$	16	☑	front + back $1920\times 1080 \times 65$ classes
03_2023-04-11	spring	day	$638$	$1920\times 1080$	$1920\times 1080$	16	☑	front + back $1920\times 1080 \times 65$ classes
11_2023-04-13	spring	night	$631$	$1920\times 1080$	$1920\times 1080$	16	☑	front + back $1920\times 1080 \times 65$ classes

6 DoF poses obtained using ALeGO-LOAM localization method refined with Interactive SLAM.

Sensors

Sensor	Model	Resolution
Front cam	ZED (stereo)	$1920\times 1080$
Back cam	RealSense D435	$1920\times 1080$
LiDAR	VLP-16	$16\times 1824$

Semantics

Semantic masks are obtained using the Oneformer pre-trained on the Mapillary dataset.

The masks are stored as mono-channel images.Each pixel stores a semantic label. Examples of semantic information are shown in the table below:

Label	Semantic class	Color, [r, g, b]
...	...	...
10	Parking	[250, 170, 160]
11	Pedestrin Area	[96, 96, 96]
12	Rail Track	[230, 150, 140]
13	Road	[128, 64, 128]
...	...	...

The complete list of semantic labels and their colors are described in the file anno_config.json.

An example of a mask over the image:

Structure

The data are organized by tracks, the length of one track is about 3 km, each track includes about 600 frames. The distance between adjacent frames is ~5 m.

The structure of track data storage is as follows:

00_2023-02-21
├── back_cam
│   ├── ####.png
│   └── ####.png
├── demo.mp4
├── front_cam
│   ├── ####.png
│   └── ####.png
├── labels
│   ├── back_cam
│   │   ├── ####.png
│   │   └── ####.png
│   └── front_cam
│       ├── ####.png
│       └── ####.png
├── lidar
│   ├── ####.bin
│   └── ####.bin
├── test.png
├── track.csv
└── track_map.png

where

• #### - file name, which is the timestamp of the image/scan (virtual timestamp of the moment when the image/scan was taken)
• .bin - files - LiDAR scans in binary format
• .png - images and semantic masks
• .csv - timestamp mapping for all data and 6DoF robot poses

An example of a track trajectory (track_map.png):