This Innovative to Practice Work in Progress paper presents an approach to obtain synergy by implementing an integrated mechatronic project within a postgraduate program. Mechatronics has been a reference for synergetic integration of technology. However, synergy is obtained not only from technical issues, but through professional skills. Nevertheless, traditional education and industry standards focus only on the technical side. The study case is performed within the program Integrated Design of Technical Systems by implementing strategies such as integrated scheduling, integrated assessment and project proposal with application fields such as agriculture, robotics, health and mobility. This approach does not require additional credits for a project course, since the project lies underneath the courses. It has led to an increase of enrollment of students, which is significant during times when less engineers opt for further technical education.

This paper presents a purely visual semantic mapping framework using omnidirectional images. The approach rests upon the robust segmentation of the robot's local free space, replacing conventional range sensors for the generation of occupancy grid maps. The perceptions are mapped into a bird's eye view allowing an inverse sensor model directly by removing the non-linear distortions of the omnidirectional camera mirror. The system relies on a place category classifier to label the navigation relevant categories: room, corridor, doorway, and open room. Each place class maintains a separated grid map that are fused with the range-based occupancy grid for building a dense semantic map.

This paper presents a spatial layout recovery approach from single omnidirectional images. Vertical structures in the scene are extracted via classification from heterogeneous features computed at the superpixel level. Vertical surfaces are further classified according to their main orientation by fusing oriented line features, floor-wall boundary features and histogram of oriented gradients (HOG) with a Random Forest classifier. Oriented line features are used to build an orientation map that considers the main vanishing points. The floor-wall boundary feature attempts to reconstruct the scene shape as if it were observed from a bird's-eye view. Finally, the HOG descriptors are aggregated per superpixel and summarize the gradient distribution at homogeneous appearance regions. Compared to existing methods in the literature which rely only on corners or lines, our method gains statistical support from multiple cues aggregated per superpixel which provide more robustness against noise, occlusion, and clutter.