A Deep Learning approach to Instance Segmentation of indoor environment

Nowadays, mobile robots are frequently used in both indoor and outdoor situations, including agriculture, transportation in industries, surveillance, and cleaning buildings. These are being developed for several applications where long-term capabilities would be advantageous. The primary goal of mobile robotics is to build fully autonomous machines, meaning that they must be able to carry out their jobs without assistance from humans. Their industrial and technical use is continuously becoming more significant, especially when reliability (the uninterrupted and dependable completion of tasks like surveillance), accessibility (the inspection of locations that are inaccessible to humans, such as confined spaces, hazardous environments, or remote sites), or cost are considered. Computer vision is playing a vital part in making these projects more efficient due to the enormous strides that Machine Learning and Deep Learning have achieved in the sector. These innovations significantly altered how tracking and detecting issues are tackled, making real-world applications considerably more practical and successful. The goal of this thesis is to investigate a system that can segment floor plans into individual rooms. Several robotics activities depend on this, including topological mapping, semantic mapping, place categorization, human-robot interaction, and automated commercial cleaning. Different map partitioning strategies can be used to complete this task. The Mask R-CNN model has been used to fulfill this target successfully. This network, an extension of Faster R-CNN, enables the prediction of an object mask in conjunction with the branch already in place for bounding box recognition. Since there is not a reasonably large, publicly accessible dataset of floor plans, this thesis's research involved creating one with the corresponding annotations. The entire dataset containing 4224 images is then used to train the Mask R-CNN model, allowing us to obtain a neural network capable of performing an instance segmentation task on them. Once the model has been trained and validated, a new floor plan map is produced using measurements from a LIDAR sensor. Then, the map is processed using computer vision software to create a crisper and cleaner map of the surrounding area and to prepare it for the segmentation method. This work can be used as a foundation for creating more sophisticated systems capable of automatically classifying rooms (for instance, by including various room typologies in the dataset), or by integrating the algorithm onto a mobile robot to perform segmentation after mapping an entire area.