Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area

Semantische Segmentierung optischer Sensordaten für Anwendungen in der Binnenschifffahrt

Inland waterway transport (IWT) is an extremely important backbone for heavy good transportation with severe economical influence and the potential for the reduction oftraffic-related greenhouse gas emission. As IWT is expected to increase, updated chart data is required. Traditional survey methods are intense in cost and time. This work presents a processing scope for self-updating inland waterway charts. The required data can be gathered through optical sensors, that are fitted on IWT vessels. In semantic segmentation, every pixel in a RGB image is assigned to a defined class. This machine-learning problem is used to distinguish between various objects in a (IWT related) scene and thus to survey the infrastructure. For this task, the new BerlinIWT dataset is proposed. Existing datasets in this field may contain more examples, but do not provide an adequate number of classes. Training a neural network on the datasets MaSTr1325 and BerlinIWT leads to remarkable results. Spatial mapping information is completed with LiDAR (light detection and ranging) data. The acquired 3D point clouds provide precise distance information with a reasonable maximum range. The sensor compensates the flaws of (stereo) cameras, that are suitable for scene understanding, but inappropriate for distance measurements. The most suitable technique for the combination of LiDAR and camera data is discussed. For the ongoing scope towards simultaneous localisation and mapping (SLAM), two different methods for optical flow estimation are compared. Finally, further processing steps are pointed out and the application is discussed with respect to a traffic-telematics related use-case