We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations. The resulting model can predict future motions of an articulated object with high confidence because of the spatial and temporal structure. We demonstrate the effectiveness of the predictive model by incorporating it within a standard simultaneous localization and mapping (SLAM) pipeline for mapping and robot localization in previously unexplored dynamic environments. Our method is able to localize the robot and map a dynamic scene by explaining the observed motion in the world. We demonstrate the effectiveness of the proposed framework for both simulated and real-world dynamic environments.
This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics. 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.