In the field of automated working machines, not only is the general trend towards automation in industry, transport and logistics reflected, but new areas of application and markets are also constantly emerging. In this paper we present a pipeline for terrain classification in offroad environments and in the field of "automated maintenance of slopes", which offers potential for solving numerous socio-economic needs. Working tasks can be made more efficient, more ergonomic and, in particular, much safer, because mature, automated vehicles are used. At present, however, such tasks can only be carried out remotely or semi-automatically, under the supervision of a trained specialist. This only partially facilitates the work. The real benefit only comes when the supervising person is released from this task and is able to pursue other work. In addition to the development of a safe integrated system and sensor concept for use in public spaces as a basic prerequisite for vehicles licensed in the future, increased situational awareness of mobile systems through machine learning in order to increase their efficiency and flexibility is also of great importance.
In the past decades, developments in the field of computer vision have made both the software and hardware more capable and more easily accessible. This has enabled otherwise complex vision systems to be used in other fields, such as autonomous robotics. Although vision systems in the visible light spectrum are commonplace in robotics nowadays, the thermal spectrum is still rarely used, even though it offers certain advantages. A thermal camera can sense the temperature of objects, is independent of illumination and can actually see through heavy smoke and fog. This makes it a useful tool in particular in the field of rescue robotics, where poor vision conditions are to be expected. In this paper, the feasibility of using two thermal cameras in a stereo vision setup to map indoor scenes is to be examined. It is meant to allow an autonomous robot to perceive its indoor surroundings as a 3D space, even in poor vision conditions. The biggest challenges are the calibration of thermal cameras and the proper filtering of the raw image and the resulting disparity map. Simple and easily implemented solutions are proposed for each of these issues.