  Object SLAM is considered increasingly significant for robot high-level
perception and decision-making. Existing studies fall short in terms of data
association, object representation, and semantic mapping and frequently rely on
additional assumptions, limiting their performance. In this paper, we present a
comprehensive object SLAM framework that focuses on object-based perception and
object-oriented robot tasks. First, we propose an ensemble data association
approach for associating objects in complicated conditions by incorporating
parametric and nonparametric statistic testing. In addition, we suggest an
outlier-robust centroid and scale estimation algorithm for modeling objects
based on the iForest and line alignment. Then a lightweight and object-oriented
map is represented by estimated general object models. Taking into
consideration the semantic invariance of objects, we convert the object map to
a topological map to provide semantic descriptors to enable multi-map matching.
Finally, we suggest an object-driven active exploration strategy to achieve
autonomous mapping in the grasping scenario. A range of public datasets and
real-world results in mapping, augmented reality, scene matching,
relocalization, and robotic manipulation have been used to evaluate the
proposed object SLAM framework for its efficient performance.
