KTH Moving Objects Dataset

KTH Scitos G5 robot - Rosie
This dataset extends KTH Longterm Dataset Labels with more locations within the same office environment at KTH. The dataset contains a subset of the labels and these objects are consistently located in different positions in multiple rooms. The label subset annotated in this dataset is {chair6, chair1, water_boiler, backpack1, pillow, trash_bin, backpack3, chair2, hanger_jacket, backpack2}. Each observation consists of a set of 17 RGB-D images 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 WayPoint10

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 (~ 6 GB). As an alternative, the individual folders and files can be obtained from here, and would have to be downloaded manually.


Nils Bore
SE-100 44 Stockholm