Manipulator and object tracking for in-hand 3D object modeling

Recognizing and manipulating objects is an important task for mobile robots performing useful services in everyday environments. While existing techniques for object recognition related to manipulation provide very good results even for noisy and incomplete data, they are typically trained using data generated in an offline process. As a result, they do not enable a robot to acquire new object models as it operates in an environment. In this paper we develop an approach to building 3D models of unknown objects based on a depth camera observing the robot's hand while moving an object. The approach integrates both shape and appearance information into an articulated Iterative Closest Point approach to track the robot's manipulator and the object. Objects are modeled by sets of surfels, which are small patches providing occlusion and appearance information. Experiments show that our approach provides very good 3D models even when the object is highly symmetric and lacks visual features and the manipulator motion is noisy. Autonomous object modeling represents a step toward improved semantic understanding, which will eventually enable robots to reason about their environments in terms of objects and their relations rather than through raw sensor data.