Enhancing geometric maps through environmental interactions

The deployment of rescue robots in real operations is becoming increasingly common thanks to recent advances in AI technologies and high performance hardware. Rescue robots can now operate for extended period of time, cover wider areas and process larger amounts of sensory information making them considerably more useful during real life threatening situations, including both natural or man-made disasters. In this thesis we present results of our research which focuses on investigating ways of enhancing visual perception for Unmanned Ground Vehicles (UGVs) through environmental interactions using different sensory systems, such as tactile sensors and wireless receivers.We argue that a geometric representation of the robot surroundings built upon vision data only, may not suffice in overcoming challenging scenarios, and show that robot interactions with the environment can provide a rich layer of new information that needs to be suitably represented and merged into the cognitive world model. Visual perception for mobile ground vehicles is one of the fundamental problems in rescue robotics. Phenomena such as rain, fog, darkness, dust, smoke and fire heavily influence the performance of visual sensors, and often result in highly noisy data, leading to unreliable or incomplete maps.