  This paper presents the first active object mapping framework for complex
robotic manipulation and autonomous perception tasks. The framework is built on
an object SLAM system integrated with a simultaneous multi-object pose
estimation process that is optimized for robotic grasping. Aiming to reduce the
observation uncertainty on target objects and increase their pose estimation
accuracy, we also design an object-driven exploration strategy to guide the
object mapping process, enabling autonomous mapping and high-level perception.
Combining the mapping module and the exploration strategy, an accurate object
map that is compatible with robotic grasping can be generated. Additionally,
quantitative evaluations also indicate that the proposed framework has a very
high mapping accuracy. Experiments with manipulation (including object grasping
and placement) and augmented reality significantly demonstrate the
effectiveness and advantages of our proposed framework.
