An experimental study of four variants of pose clustering from dense range data

Parameter clustering is a robust estimation technique based on location statistics in a parameter space where parameter samples are computed from data samples. This article investigates parameter clustering as a global estimator of object pose or rigid motion from dense range data without knowing correspondences between data points. Four variants of the algorithm are quantitatively compared regarding estimation accuracy and robustness: sampling poses from data points or from points with surface normals derived from them, each combined with clustering poses in the canonical or consistent parameter space, as defined in Hillenbrand (2007). An extensive test data set is employed: synthetic data generated from a public database of three-dimensional object models through various levels of corruption of their geometric representation; real range data from a public database of models and cluttered scenes. It turns out that sampling raw data points and clustering in the consistent parameter space yields the estimator most robust to data corruption. For data of sufficient quality, however, sampling points with normals is more efficient; this is most evident when detecting objects in cluttered scenes. Moreover, the consistent parameter space is always preferable to the canonical parameter space for clustering

Sequential Re-planning for Dextrous Grasping Under Object-pose Uncertainty

Abstract — This work shows how successive grasp attempts can be re-planned to make use of tactile information acquired during previous grasp attempts. Our main contributions are to enable planning of dexterous grasping for high degree of freedom manipulators, and belief updating from tactile sensors in 6 dimensional space. The method is demonstrated in trials with simulated robots. Sequential re-planning is shown to achieve a greater success rate than single grasp attempts, and trajectories that maximise information gain require less replanning iterations than conventional trajectories before a grasp is achieved. I

Sequential Trajectory Re-planning with Tactile Information Gain for Dexterous Grasping under Object-pose Uncertainty

Abstract — Dexterous grasping of objects with uncertain pose is a hard unsolved problem in robotics. This paper solves this problem using information gain re-planning. First we show how tactile information, acquired during a failed attempt to grasp an object can be used to refine the estimate of that object’s pose. Second, we show how this information can be used to replan new reach to grasp trajectories for successive grasp attempts. Finally we show how reach-to-grasp trajectories can be modified, so that they maximise the expected tactile information gain, while simultaneously delivering the hand to the grasp configuration that is most likely to succeed. Our main novel outcome is thus to enable tactile information gain planning for Dexterous, high degree of freedom (DoFs) manipulators. We achieve this using a combination of information gain planning, hierarchical probabilistic roadmap planning, and belief updating from tactile sensors for objects with non-Gaussian pose uncertainty in 6 dimensions. The method is demonstrated in trials with simulated robots. Sequential replanning is shown to achieve a greater success rate than single grasp attempts, and trajectories that maximise information gain require fewer re-planning iterations than conventional planning methods before a grasp is achieved. I

A Comparison and Evaluation of Three Different Pose Estimation Algorithms In Detecting Low Texture Manufactured Objects

This thesis examines the problem of pose estimation, which is the problem of determining the pose of an object in some coordinate system. Pose refers to the object\u27s position and orientation in the coordinate system. In particular, this thesis examines pose estimation techniques using either monocular or binocular vision systems. Generally, when trying to find the pose of an object the objective is to generate a set of matching features, which may be points or lines, between a model of the object and the current image of the object. These matches can then be used to determine the pose of the object which was imaged. The algorithms presented in this thesis all generate possible matches and then use these matches to generate poses. The two monocular pose estimation techniques examined are two versions of SoftPOSIT: the traditional approach using point features, and a more recent approach using line features. The algorithms function in very much the same way with the only difference being the features used by the algorithms. Both algorithms are started with a random initial guess of the object\u27s pose. Using this pose a set of possible point matches is generated, and then using these matches the pose is refined so that the distances between matched points are reduced. Once the pose is refined, a new set of matches is generated. The process is then repeated until convergence, i.e., minimal or no change in the pose. The matched features depend on the initial pose, thus the algorithm\u27s output is dependent upon the initially guessed pose. By starting the algorithm with a variety of different poses, the goal of the algorithm is to determine the correct correspondences and then generate the correct pose. The binocular pose estimation technique presented attempts to match 3-D point data from a model of an object, to 3-D point data generated from the current view of the object. In both cases the point data is generated using a stereo camera. This algorithm attempts to match 3-D point triplets in the model to 3-D point triplets from the current view, and then use these matched triplets to obtain the pose parameters that describe the object\u27s location and orientation in space. The results of attempting to determine the pose of three different low texture manufactured objects across a sample set of 95 images are presented using each algorithm. The results of the two monocular methods are directly compared and examined. The results of the binocular method are examined as well, and then all three algorithms are compared. Out of the three methods, the best performing algorithm, by a significant margin, was found to be the binocular method. The types of objects searched for all had low feature counts, low surface texture variation, and multiple degrees of symmetry. The results indicate that it is generally hard to robustly determine the pose of these types of objects. Finally, suggestions are made for improvements that could be made to the algorithms which may lead to better pose results