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Abstract: "This thesis presents a method for incrementally recognizing objects as they are scanned by range sensors mounted on a mobile platform, such as a construction, mining, or agricultural field robot. The method enhances the productivity of field robotic machines in these settings by allowing them to start planning and moving toward the object before scanning is complete, or execute other motion tasks without the need to stop and scan. The system consists of two components, an on-line method which accomplishes the recognition and an off-line method which generates a finite state machine and associated parameters which guide the process of incremental recognition. The online method handles range data from laser or radar range sensors and is robust to the noise and poor sensor data that can result from unmeasured sensor motion during scanning. The off-line method uses range data obtained by simulating a range sensor scanning the object model in a sequence of poses. The on-line component of the system is used by an automated machine to recognize and locate objects it must interact with in its work area. Since this method handles unmeasured sensor motion, costs of these automated systems can be reduced by eliminating the need for highly accurate positioning systems to compensate for motion during scanning. Objects that can be recognized and localized with this method may consist of planar surface patches that meet at boundaries or planar surface patches with dangling boundaries. Object surfaces and boundaries can contain variations found in industrial objects such as structural ribbing, brackets, or material clinging to the object. Material placed into the object may occlude part of the object's surfaces. The object models are stored as wire-frame models with linear segments corresponding to the boundaries of the surface patches. Additional information incorporates assumptions about the pose of an object and is referenced to the object model. The method continuously reports the best set of matches of object model features to scene model features as the sensor data is received. For the purposes of conducting experiments and evaluating the results this thesis focuses on a specific instance of this problem, the recognition of objects used during excavation operations such as on-highway and off-highway trucks. Results are presented using range data from scanning laser and radar range sensors designed for the environment and tasks of large mobile equipment. Results are presented which show that with a single truck model the method can report incremental descriptions at a rate of 20 Hz. This method has been used in demonstrations in which a hydraulic excavator equipped with range sensors and on-board computing autonomously loads multiple trucks."
This unique book explores the important issues in studying for active visual perception. The book’s eleven chapters draw on recent important work in robot vision over ten years, particularly in the use of new concepts. Implementation examples are provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.
Automatie object recognition is a multidisciplinary research area using con cepts and tools from mathematics, computing, optics, psychology, pattern recognition, artificial intelligence and various other disciplines. The purpose of this research is to provide a set of coherent paradigms and algorithms for the purpose of designing systems that will ultimately emulate the functions performed by the Human Visual System (HVS). Hence, such systems should have the ability to recognise objects in two or three dimensions independently of their positions, orientations or scales in the image. The HVS is employed for tens of thousands of recognition events each day, ranging from navigation (through the recognition of landmarks or signs), right through to communication (through the recognition of characters or people themselves). Hence, the motivations behind the construction of recognition systems, which have the ability to function in the real world, is unquestionable and would serve industrial (e.g. quality control), military (e.g. automatie target recognition) and community needs (e.g. aiding the visually impaired). Scope, Content and Organisation of this Book This book provides a comprehensive, yet readable foundation to the field of object recognition from which research may be initiated or guided. It repre sents the culmination of research topics that I have either covered personally or in conjunction with my PhD students. These areas include image acqui sition, 3-D object reconstruction, object modelling, and the matching of ob jects, all of which are essential in the construction of an object recognition system.
Automatic object identification can be greatly augmented if the sensor provides direct incremental range (or depth) data of the object. The report investigates feasible range sensor designs for this application. A number of practical optical range sensor designs that could operate in bright sunlight are presented and analyzed quantitatively. A design is also presented of an experimental model for laboratory use, which could be used to explore the potentialities of range sensing for automatic object identification. (Author).
This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.