KTH Longterm Dataset Labels

KTH Scitos G5 robot - Rosie
This dataset contains a subset of observations from this dataset - 88 observations acquired at WayPoint16. Each observation consists of a set of 17 RGB-D images (originally 51, however this dataset only contains the RGB-D clouds corresponding to a single height value of the PTU) obtained by moving the pan-tilt in a horizontal pattern, in increments of 20 degrees. In addition to the raw sensor data, each observation contains object annotations (masks and labels). The data is a part of the Strands EU FP7 project.

Dataset structure

The data is structured in folders as follows: YYYYMMDD/patrol_run_YYY/room_ZZZ , where: Each folder of the type YYYMMDD/patrol_run_YYY/room_ZZZ contains the following files: The description of the room.xml file accompanying an observation can be found here.

Each object xml file ( rgb_XXXX_label_#.xml) contains the following data:


A parser is provided here (can be installed with sudo apt-get install ros-indigo-metaroom-xml-parser) which reads in the data and returns C++ data structures encapsulating the low-level data from the disk. Form more information please refer to the parser README ( or here for a list of supported methods). Information about how to use the Strands package repository can be found here.

Assuming the parser has been installed, the labelled data can be visualized with the following sample application:

rosrun metaroom_xml_parser load_labelled_data /path/to/data WayPoint16

This loads all the observations along with the labelled data from the path specified and displays each observation along with the corresponding labelled objects in a visualizer window.
Observation (red) with labelled objects (RGB)


For more information on the labelling tool used, please refer to this page.


This dataset is available for download in a single archive file (~ 15 GB). As an alternative, the individual folders and files can be obtained from here, and would have to be downloaded manually.

Condition of use

If you use the dataset for your research, please cite our paper that describes it:

	Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario 
	Ambrus, Rares and Ekekrantz, Johan and Folkesson, John and Jensfelt, Patric
	Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on
We attached a bibtex record for your convenience.