  Object SLAM uses additional semantic information to detect and map objects in
the scene, in order to improve the system's perception and map representation
capabilities. Quadrics and cubes are often used to represent objects, but their
single shape limits the accuracy of object map and thus affects the application
of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape
parameters into SLAM for representing objects, and propose a separate parameter
estimation method that can accurately estimate object pose and adapt to
different shapes. Furthermore, we present a lightweight data association
strategy for correctly associating semantic observations in multiple views with
object landmarks. We implement a monocular semantic SLAM system with real-time
performance and conduct comprehensive experiments on public datasets. The
results show that our method is able to build accurate object map and has
advantages in object representation. Code will be released upon acceptance.
