  For robots to navigate and interact more richly with the world around them,
they will likely require a deeper understanding of the world in which they
operate. In robotics and related research fields, the study of understanding is
often referred to as semantics, which dictates what does the world "mean" to a
robot, and is strongly tied to the question of how to represent that meaning.
With humans and robots increasingly operating in the same world, the prospects
of human-robot interaction also bring semantics and ontology of natural
language into the picture. Driven by need, as well as by enablers like
increasing availability of training data and computational resources, semantics
is a rapidly growing research area in robotics. The field has received
significant attention in the research literature to date, but most reviews and
surveys have focused on particular aspects of the topic: the technical research
issues regarding its use in specific robotic topics like mapping or
segmentation, or its relevance to one particular application domain like
autonomous driving. A new treatment is therefore required, and is also timely
because so much relevant research has occurred since many of the key surveys
were published. This survey therefore provides an overarching snapshot of where
semantics in robotics stands today. We establish a taxonomy for semantics
research in or relevant to robotics, split into four broad categories of
activity, in which semantics are extracted, used, or both. Within these broad
categories we survey dozens of major topics including fundamentals from the
computer vision field and key robotics research areas utilizing semantics,
including mapping, navigation and interaction with the world. The survey also
covers key practical considerations, including enablers like increased data
availability and improved computational hardware, and major application areas
where...
