Model Of Unknown Object Research Articles

Inferring 3D Shapes of Unknown Rigid Objects in Clutter Through Inverse Physics Reasoning

We present a probabilistic approach for building, on the fly, 3-D models of unknown objects while being manipulated by a robot. We specifically consider manipulation tasks in piles of clutter that contain previously unseen objects. Most manipulation algorithms for performing such tasks require known geometric models of the objects in order to grasp or rearrange them robustly. One of the novel aspects of this work is the utilization of a physics engine for verifying hypothesized geometries in simulation. The evidence provided by physics simulations is used in a probabilistic framework that accounts for the fact that mechanical properties of the objects are uncertain. We present an efficient algorithm for inferring occluded parts of objects based on their observed motions and mutual interactions. Experiments using a robot show that this approach is efficient for constructing physically realistic 3-D models, which can be useful for manipulation planning. Experiments also show that the proposed approach significantly outperforms alternative approaches in terms of shape accuracy.

A novel algorithm for fast grasping of unknown objects using C-shape configuration

Increasing grasping efficiency is very important for the robots to grasp unknown objects especially subjected to unfamiliar environments. To achieve this, a new algorithm is proposed based on the C-shape configuration. Specifically, the geometric model of the used under-actuated gripper is approximated as a C-shape. To obtain an appropriate graspable position, this C-shape configuration is applied to fit geometric model of an unknown object. The geometric model of unknown object is constructed by using a single-view partial point cloud. To examine the algorithm using simulations, a comparison of the commonly used motion planners is made. The motion planner with the highest number of solved runs, lowest computing time and the shortest path length is chosen to execute grasps found by this grasping algorithm. The simulation results demonstrate that excellent grasping efficiency is achieved by adopting our algorithm. To validate this algorithm, experiment tests are carried out using a UR5 robot arm and an under-actuated gripper. The experimental results show that steady grasping actions are obtained. Hence, this research provides a novel algorithm for fast grasping of unknown objects.

Open Intelligence on Learning of Motion Models of Unknown Objects

Open Intelligence on Learning of Motion Models of Unknown Objects

Automatic 3D model acquisition for unknown objects based on hybrid vision technology

Three-dimensional (3D) model acquisition is the process of building a 3D model of an object. But due to the limited field of view of the scanner, this task is mainly performed by taking several scans with human intervention. In order to make the 3D modeling process efficient, a novel automatic 3D modeling method for unknown objects based on hybrid vision technology in a binocular structured light system (BSLS) is proposed. Firstly, the limit visual vacuums of the BSLS are established, and they will be used to predict the unknown area with an acquired 2.5D range image. With the 2D intensity image acquired synchronously, the coarse boundary size is recovered from Shape from Shading, and it leads the prediction of the unknown area to be more precise. Based on the combination of the predicted contours, the next best viewpoint is determined with more unknown areas visible. The proposed method can be used to obtain the 3D models of unknown objects automatically, and the experimental results illustrate the validity and efficiency of our approach.

View/state planning for three-dimensional object reconstruction under uncertainty

We propose a holistic approach for three-dimensional (3D) object reconstruction with a mobile manipulator robot with an eye-in-hand sensor; considering the plan to reach the desired view/state, and the uncertainty in both observations and controls. This is one of the first methods that determines the next best view/state in the state space, following a methodology in which a set of candidate views/states is directly generated in the state space, and later only a subset of these views is kept by filtering the original set. It also determines the controls that yield a collision free trajectory to reach a state using rapidly-exploring random trees. To decrease the processing time we propose an efficient evaluation strategy based on filters, and a 3D visibility calculation with hierarchical ray tracing. The next best view/state is selected based on the expected utility, generating samples in the control space based on an error distribution according to the dynamics of the robot. This makes the method robust to positioning error, significantly reducing the collision rate and increasing the coverage, as shown in the experiments. Several experiments in simulation and with a real mobile manipulator robot with 8 degrees of freedom show that the proposed method provides an effective and fast method for a mobile manipulator to build 3D models of unknown objects. To our knowledge, this is one of the first works that demonstrates the reconstruction of complex objects with a real mobile manipulator considering uncertainty in the controls.

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.

Vision Sensor Planning for 3–D Model Acquisition

A novel method is proposed in this paper for automatic acquisition of three-dimensional (3-D) models of unknown objects by an active vision system, in which the vision sensor is to be moved from one viewpoint to the next around the target to obtain its complete model. In each step, sensing parameters are determined automatically for incrementally building the 3-D target models. The method is developed by analyzing the target's trend surface, which is the regional feature of a surface for describing the global tendency of change. While previous approaches to trend analysis are usually focused on generating polynomial equations for interpreting regression surfaces in three dimensions, this paper proposes a new mathematical model for predicting the unknown area of the object surface. A uniform surface model is established by analyzing the surface curvatures. Furthermore, a criterion is defined to determine the exploration direction, and an algorithm is developed for determining the parameters of the next view. Implementation of the method is carried out to validate the proposed method.