Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

POSE ESTIMATION FOR ROBOTIC DISASSEMBLY USING RANSAC WITH LINE FEATURES

In this thesis, a new technique to recognize and estimate the pose of a given 3-D object from a single real image provided known prior knowledge of its approximate structure is proposed. Metrics to evaluate the correctness of a calculated pose are presented and analyzed. The traditional and the more recent approaches used in solving this problem are explored and the various methodologies adopted are discussed. The first step in disassembling a given assembly from its image is to recognize the attitude and translation of each of its constituent components - a fundamental problem which is being addressed in this work. The proposed algorithm does not depend on uniquely identifiable 3D model surface features for its operation - this makes it ideally suited for object recognition for assemblies. The algorithm works well even for low-resolution occluded object images taken under variable illumination conditions and heavy shadows and performs markedly better when these factors are removed. The algorithm uses a combination of various computer vision concepts such as segmentation, corner detection and camera calibration, and subsequently adopts a line-based object pose estimation technique (originally based on the RANSAC algorithm) to settle on the best pose estimate. The novelty of the proposed technique lies in the specific way in which the poses are evaluated in the RANSAC-like algorithm. In particular, line-based pose evaluation is adopted where the line chamfer image is used to evaluate the error distance between the projected model line and the image edges. The correctness of the computed pose is determined based on the number of line matches computed using this error distance. As opposed to the RANSAC algorithm where the search process is pseudo-random, we do an exhaustive pose search instead. Techniques to reduce the search space by a large amount are discussed and implemented. The algorithm was used to estimate the pose of 28 objects in 22 images, where some images contain multiple objects. The algorithm has been found to work with a 3-D mismatch error of less than 2.5cm in 90% of the cases and less than 1cm error in 53% of the cases in the dataset used

Individual and group dynamic behaviour patterns in bound spaces

The behaviour analysis of individual and group dynamics in closed spaces is a subject of extensive research in both academia and industry. However, despite recent technological advancements the problem of implementing the existing methods for visual behaviour data analysis in production systems remains difficult and the applications are available only in special cases in which the resourcing is not a problem. Most of the approaches concentrate on direct extraction and classification of the visual features from the video footage for recognising the dynamic behaviour directly from the source. The adoption of such an approach allows recognising directly the elementary actions of moving objects, which is a difficult task on its own. The major factor that impacts the performance of the methods for video analytics is the necessity to combine processing of enormous volume of video data with complex analysis of this data using and computationally resourcedemanding analytical algorithms. This is not feasible for many applications, which must work in real time. In this research, an alternative simulation-based approach for behaviour analysis has been adopted. It can potentially reduce the requirements for extracting information from real video footage for the purpose of the analysis of the dynamic behaviour. This can be achieved by combining only limited data extracted from the original video footage with a symbolic data about the events registered on the scene, which is generated by 3D simulation synchronized with the original footage. Additionally, through incorporating some physical laws and the logics of dynamic behaviour directly in the 3D model of the visual scene, this framework allows to capture the behavioural patterns using simple syntactic pattern recognition methods. The extensive experiments with the prototype implementation prove in a convincing manner that the 3D simulation generates sufficiently rich data to allow analysing the dynamic behaviour in real-time with sufficient adequacy without the need to use precise physical data, using only a limited data about the objects on the scene, their location and dynamic characteristics. This research can have a wide applicability in different areas where the video analytics is necessary, ranging from public safety and video surveillance to marketing research to computer games and animation. Its limitations are linked to the dependence on some preliminary processing of the video footage which is still less detailed and computationally demanding than the methods which use directly the video frames of the original footage

Development of a simulator for 3D pattern recognition scanners

Shape reconstruction using coded structured light is considered one of the most reliable techniques to recover object surfaces. The aim of the thesis was to develop a simulator capable of emulating such a model. Different types of objects were used for testing the algorithms in order to compare the results obtained with real scanners. The implementation recurs to GPU computing to take advantage of the high computational power. The thesis was carried out in collaboration with Euclid Labs.ope