A human supervisory approach to modeling industrial scenes using geometric primitives

Abstract

A three-dimensional world model is crucial for many robotic tasks. Modeling techniques tend to be either fully manual or autonomous. Manual methods are extremely time consuming but also highly accurate and flexible. Autonomous techniques are fast but inflexible and, with real-world data, often inaccurate. The method presented in this paper combines the two, yielding a highly efficient, flexible, and accurate mapping tool. The segmentation and modeling algorithms that compose the method are specifically designed for industrial environments, and are described in detail. A mapping system based on these algorithms has been designed. It enables a human supervisor to quickly construct a fully defined world model from unfiltered and unsegmented real-world range imagery. Examples of how industrial scenes are modeled with the mapping system are provided.