To successfully manipulate in unknown environments, a robot must be able to perceive degrees of freedom of objects in its environment. Based on the resulting kinematic model and joint configurations, the robot is able to select and adapt actions, recognize their successful completion and detect failure. We present an RGB-D-based online algorithm for the interactive perception of articulated objects. The algorithm decomposes the perception problem into three interconnected levels of recursive estimation. The estimation problems at each level are much simpler than the original problem and their robustness is improved by level-specific priors that help reject noise in the measurements. These three estimators mutually inform each other to further improve the convergence properties of the three estimation solutions. We demonstrate that the resulting algorithm is robust, accurate, and versatile in realworld experiments. We also show how the perceptual skill can be used online to control the robot's behavior in real-world manipulation tasks.

Depth sensors based on projected structured light have become standard in robotics research. However, when several of these sensors share the same workspace, the measurement quality can deteriorate significantly due to interference of the projected light patterns. We present a comprehensive study of this effect in Kinect and Xtion RGB-D sensors. In particular, our study investigates the effect of measurement failure due to interference. Our experiments show that up to 95% of the depth measurements in the interference image region can disappear when two RGB-D sensors interfere with each other. We determine the severity of interference as a function of relative sensor placement and propose simple guidelines to reduce the impact of sensor interference. We show that these guidelines greatly increase the robustness of RGB-D-based SLAM.