Object SLAM-Based Active Mapping and Robotic Grasping
This work addresses the problem of generating accurate object maps for robotic grasping and manipulation tasks, enabling more effective autonomous perception and manipulation for robots.
This paper introduces an active object mapping framework that integrates an object SLAM system with multi-object pose estimation, specifically optimized for robotic grasping. The framework uses an object-driven exploration strategy to reduce observation uncertainty and improve pose estimation accuracy, resulting in a highly accurate object map for robotic manipulation.
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.