J. Hoch

Bio: J. Hoch is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Image segmentation & Feature extraction. The author has an hindex of 1, co-authored 1 publications receiving 17 citations.

Towards shape-based visual object categorization for humanoid robots

Abstract: Humanoid robots should be able to grasp and handle objects in the environment, even if the objects are seen for the first time. A plausible solution to this problem is to categorize these objects into existing classes with associated actions and functional knowledge. So far, efforts on visual object categorization using humanoid robots have either been focused on appearance-based methods or have been restricted to object recognition without generalization capabilities. In this work, a shape model-based approach using stereo vision and machine learning for object categorization is introduced. The state-of-the-art features for shape matching and shape retrieval were evaluated and selectively transfered into the visual categorization. Visual sensing from different vantage points allows the reconstruction of 3D mesh models of the objects found in the scene by exploiting knowledge about the environment for model-based segmentation and registration. These reconstructed 3D mesh models were used for shape feature extraction for categorization and provide sufficient information for grasping and manipulation. Finally, the visual categorization was successfully performed with a variety of features and classifiers allowing proper categorization of unknown objects even when object appearance and shape substantially differ from the training set. Experimental evaluation with the humanoid robot ARMAR-IIIa is presented.

Data-Driven Grasp Synthesis—A Survey

Abstract: We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

Ensemble of shape functions for 3D object classification

Abstract: This work addresses the problem of real-time 3D shape based object class recognition, its scaling to many categories and the reliable perception of categories. A novel shape descriptor for partial point clouds based on shape functions is presented, capable of training on synthetic data and classifying objects from a depth sensor in a single partial view in a fast and robust manner. The classification task is stated as a 3D retrieval task finding the nearest neighbors from synthetically generated views of CAD-models to the sensed point cloud with a Kinect-style depth sensor. The presented shape descriptor shows that the combination of angle, point-distance and area shape functions gives a significant boost in recognition rate against the baseline descriptor and outperforms the state-of-the-art descriptors in our experimental evaluation on a publicly available dataset of real-world objects in table scene contexts with up to 200 categories.

Simultaneous Grasp and Motion Planning: Humanoid Robot ARMAR-III

Abstract: In this work, we present an integrated approach for planning collision-free grasping motions. Therefore, rapidly exploring random tree (RRT)-based algorithms are used to build a tree of reachable and collision-free configurations. During tree generation, both grasp hypotheses and approach movements toward them are computed. The quality of reachable grasping poses is evaluated using grasp wrench space (GWS) analysis. We present an extension to a dual-arm planner that generates bimanual grasps together with collision-free dual-arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III (Figure 1).

Shape-based depth image to 3D model matching and classification with inter-view similarity

Abstract: Object recognition and especially object class recognition is and will be a key capability in home robotics when robots have to tackle manipulation tasks and grasp new objects or just have to search for objects. The goal is to have a robot classify 'never before seen objects' at first occurrence in a single view in a fast and robust manner. The classification task can be seen as a matching problem, finding the most appropriate 3D model and view with respect to a given depth image. We introduce a single-view shape model based classification approach using RGB-D sensors and a novel matching procedure for depth image to 3D model matching leading inherently to object classification. Utilizing the inter-view similarity of the 3D models for enhanced matching, the average precision of our descriptors is increased of up to 15% resulting in high classification accuracy. The presented adaptation of 3D shape descriptors to 2.5D data enables us to calculate the features in real time, directly from the 3D points of the sensor, without any calculation of normals or generating a mesh from it which is typical of state-of-art methods. Furthermore, we introduce a semi-automatic, user-centric approach to utilize the Internet for acquiring the required training data in the form of 3D models which significantly reduces the time for teaching new categories.

Perception and Grasping of Object Parts from Active Robot Exploration

Abstract: Like humans, robots that need semantic perception and accurate estimation of the environment can increase their knowledge through active interaction with objects. This paper proposes a novel method for 3D object modeling for a robot manipulator with an eye-in-hand laser range sensor. Since the robot can only perceive the environment from a limited viewpoint, it actively manipulates a target object and generates a complete model by accumulation and registration of partial views. Three registration algorithms are investigated and compared in experiments performed in cluttered environments with complex rigid objects made of multiple parts. A data structure based on proximity graph, that encodes neighborhood relations in range scans, is also introduced to perform efficient range queries. The proposed method for 3D object modeling is applied to perform task-level manipulation. Indeed, once a complete model is available the object is segmented into its constituent parts and categorized. Object sub-parts that are relevant for the task and that afford a grasping action are identified and selected as candidate regions for grasp planning.