The Object Labeling Toolkit (OLT) consists of a number of software applications for performing an effortless labeling of datasets, concretely those containing sequences of RGB-D observations.
If you use this software, please cite it through:
For getting the toolkit and further information please check the project webpage at GitHub. OLT aims at facilitate the labeling of arbitrarily large sequences of RGB-D observations. For that, the point clouds from the RGB-D observations are registered in order to reconstruct the captured scene. Then, the toolkit provides an application to easily annotate objects within that reconstruction by fitting boxes to them:
Example of a kitchen scene being annotated.
These annotations are finally propagated to each individual RGB-D observation in the sequence, resulting in a dense labeling of their RGB and depth information.
The main software components (appliations) of the toolkit are:
- Process_rawlog: Sets the extrinsic and intrinsic parameters of the sensors used within the dataset.
- Localization: Localizes the poses/locations from which the RGB-D observations of the datset were taken.
- Sequential_visualization: Visually shows a 3D reconstruction of the collected data, and stores it as a scene.
- Label_scene: Permits us to effortlessly label a reconstructed scene.
- Label_rawlog: Propagates the annotated labels in a scene to each RGB-D observation (to both their RGB and depth information) within the dataset.
- Dataset_statistics: Shows information of the dataset, e.g. a summary of the objects appearing on it, number of times that they appear, number of pixels they occupy, etc.
- Create_video & Segmentation: Experimental applications under development.
The toolkit resorts to two widely used libraries in robotics: