3-D Geometric Model Acquisition System for a Tele-Collaboration Testbed

2000 ◽  
pp. 15-28
Author(s):  
Ruzena Bajcsy ◽  
Reyes Enciso ◽  
Gerda Kamberova ◽  
Lucien Nocera ◽  
Radim Sara
Keyword(s):  
Geometric Model ◽  
Acquisition System ◽  
Model Acquisition

scholarly journals Object segmentation methods for online model acquisition to guide robotic grasping

2021 ◽  
Author(s):  
Dmitri Ignakov
Keyword(s):  
Object Segmentation ◽  
Vision System ◽  
Geometric Model ◽  
Graph Cuts ◽  
Segmentation Algorithm ◽  
Service Robots ◽  
Segmentation Method ◽  
Robotic Grasping ◽  
Model Acquisition ◽  
Segmentation Methods

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. . First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method also models the distribution of 3D points in space as a distribution of algebraic distances from an ellipsoid fitted to the object, improving the method's ability to predict which points are likely to belong to the object or the background. Experimental validation of all methods is performed. Each method is evaluated in a realistic setting, utilizing scenarios of various complexities. Experimental results have demonstrated the effectiveness of the handle localization method, and the object segmentation methods.

scholarly journals Object segmentation methods for online model acquisition to guide robotic grasping

2021 ◽  
Author(s):  
Dmitri Ignakov
Keyword(s):  
Object Segmentation ◽  
Vision System ◽  
Geometric Model ◽  
Graph Cuts ◽  
Segmentation Algorithm ◽  
Service Robots ◽  
Segmentation Method ◽  
Robotic Grasping ◽  
Model Acquisition ◽  
Segmentation Methods

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. . First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method also models the distribution of 3D points in space as a distribution of algebraic distances from an ellipsoid fitted to the object, improving the method's ability to predict which points are likely to belong to the object or the background. Experimental validation of all methods is performed. Each method is evaluated in a realistic setting, utilizing scenarios of various complexities. Experimental results have demonstrated the effectiveness of the handle localization method, and the object segmentation methods.

ALGEBRA OF PREDICATES AND RELATED GEOMETRIC MODELS CREATION IN REGARDS OF UNIFIED STATE EXAM IN INFORMATICS (RUSSIAN EGE)

2019 ◽  
pp. 40-47
Author(s):  
E. A. Mironchik
Keyword(s):  
Geometric Model ◽  
Main Idea ◽  
Formal Logic ◽  
High Complexity ◽  
Geometric Models ◽  
Complexity Problem ◽  
Logical Expression ◽  
State Examination

The article discusses the method of solving the task 18 on the Unified State Examination in Informatics (Russian EGE). The main idea of the method is to write the conditions of the problem utilizing the language of formal logic, using elementary predicates. According to the laws of logic the resulting complex logical expression would be transformed into an expression, according to which a geometric model is supposed to be constructed which allows to obtain an answer. The described algorithm does allow high complexity problem to be converted into a simple one.

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