What is Semantic Mapping in the context of autonomous robots?
Semantic mapping, in the context of autonomous vehicles/robots, involves creating a representation of the world or environment so vehicles can understand the meaning of different objects that are present. This is done using labels which can characterize certain objects – an example of this would be a robot identifying a sidewalk as an area it is prohibited from, and asphalt as a material it is able to drive on.
The current state of the technology does not capture a robot’s surroundings to a sufficient degree. A majority of the datasets used to identify objects are integrated into the program, which means new information requires complete rewriting of the code. Complicated terrains require more research – in order to fully implement semantic mapping into autonomous robots, the technology must be able to understand relationships between multiple domains. These domains may include terrain, weather, and other factors.
Why is Semantic Mapping needed?
For unconventional scenarios, robots must be implemented with a better understanding of their different observations. Specifically, off-road situations demand unique understandings of both the robot’s surroundings, and the robot’s fleet’s surroundings to string together a sufficient model of the environment.
The first application for this technology is disaster response – if the robot is autonomously and semantically able to identify that it is going through a natural disaster(earthquake, tsunami, etc.), it will be able to assist society in minimizing the damage. Similarly, semantic mapping used in search and rescue robots will greatly maximize the chance of success, while minimizing the risk involving the human rescuers.
Finally, semantic mapping may be used for military applications. Reconnaissance missions will have the capabilities of being fully autonomous, maximizing our use of military resources.
What is CAST doing to improve Semantic Mapping?
CAST uses conventional artificial intelligence based approaches, including description logics to model the entire environment. By implementing artificial intelligence into these robots, it is possible for them to make knowledgeable inferences based on rules defined in the code about their environment. CAST is also presenting how our methods of semantic mapping can be better leveraged for various applications including decision making and fleet management.