# Detection of Household Furniture Storage Space in Depth Images
## 7 Conclusions  
The goal of the presented research was to develop an algorithm that enables a service robot to detect storage space in a piece of furniture being viewed for the first time, even if that storage space is cluttered with various items. To the best of our knowledge, this is the first study to address this particular problem, which is of great importance for future-related robotic applications. A robot that is able to determine the an available storage space, and its location and size, could use this information to decide whether a particular item will fit in and plan actions to place that item in that storage space. Knowledge of the available storage capacity in the robot's work environment would allow the robot to allocate a given set of items to the designated furniture. Furthermore, we investigated the possibility of solving the considered problem using machine learning with minimal human effort. As a result of this research, we developed a method for automatically generating neural network training data that allow a neural network designed for detecting objects in depth images to detect storage volumes. The required human effort is reduced to the selection of 3D furniture models and the parameterization of the scene generation algorithm; manual annotation is not required. This is achieved by two methods, one for automatic detection of storage volumes in 3D furniture models and the other for automatic generation of realistic scenes containing these models with clutter on their shelves. The generated scenes with the associated bounding boxes of the storage volumes were used as the input dataset for training the VoteNet neural network. The trained network was used to predict the storage space bounding boxes in a dataset of images captured by a real 3D vision sensor and achieved an overall precision of 42.22%, and a recall of 66.67% for an IoU of 0.25. Moreover, it can be concluded from the performed experiments that the neural network accurately estimates the orientation of the bounding boxes, i.e., aligns them well with the edges and sides of the real storage volumes. However, the large differences in precision and recall obtained for different IoU thresholds of 0.25 and 0.5 suggest that the detector finds storage volumes approximately where they actually are, but determines their boundaries rather inaccurately. Since this was the first study on this specific problem, we consider the results obtained interesting. However, there is still much room for improvement. It would be worthwhile to investigate what performance improvement could be achieved with a larger training dataset containing more furniture models and scenes with multiple storage furniture models. In addition, the detection performance could likely be improved by creating scenes with a greater variety of background objects and objects positioned in the storage space. As storage furniture is normally relatively large and is usually perceived from a short distance away, it is expected that such objects will often be only partially visible in the captured images during the regular operation of a service robot. Detection of partially visible storage volumes and appropriate evaluation criteria for the proposed solutions are interesting problems worth investigating. Finally, in order to determine what accuracy of storage space parameters is required for practical use, some experiments with real robots picking up and placing items from and into the shelves should be conducted in the continuation of this research. Conceptualization, R.C. and M.H. ; methodology, R.C., M.H., P.P. and I.H.T. ; software, M.H., P.P. and I.H.T. ; validation, M.H. ; formal analysis, M.H. ; investigation, M.H. ; resources, M.H. and I.H.T. ; data curation, M.H. and I.H.T. ; writing--original draft preparation, R.C., M.H., P.P. and I.H.T. ; writing--review and editing, R.C., M.H., P.P. and I.H.T. ; visualization, M.H., P.P. and I.H.T. ; supervision, R.C. ; project administration, R.C. ; funding acquisition, R.C.