<title>Image accuracy and representational enhancement through low-level multisensor integration techniques</title>

摘要

This research focuses on data and conceptual enhancement algorithms. To be useful in many real-world applications, e.g., autonomous or teleoperated robotics, real-time feedback is critical. Unfortunately, many multi-sensor integration (MSI)/image processing algorithms require significant processing time. The basic direction of this research is the potentially faster and more robust formation of `clusters from pixels' rather than the slower process of extracting `clusters from images.' Techniques are evaluated on actual multi-modal sensor data obtained from a laser range camera, i.e., range and reflectance images. A suite of over thirty conceptual enhancement techniques are developed, evaluated, and compared on this sensor domain. The overall result is a general-purpose, MSI conceptual enhancement approach which can be efficiently implemented and used to supply input to a variety of high-level processes, including: object recognition, path planning, and object avoidance systems.