Automatic Configuration of the Structure and Parameterization of Perception Pipelines
| Autor: | Sebastian Albrecht, Bernd Kast, Michael Beetz, Vincent Dietrich, Michael Fiegert |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
Computer science 02 engineering and technology 010501 environmental sciences computer.software_genre ENCODE 01 natural sciences Pipeline (software) Pipeline transport Template 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Configuration space Sequential model computer 0105 earth and related environmental sciences Statistical hypothesis testing |
| Zdroj: | ICAR |
| DOI: | 10.1109/icar46387.2019.8981611 |
| Popis: | The configuration of perception pipelines is a complex procedure that requires substantial amounts of engineering effort and knowledge. A pipeline consists of interconnected individual perception operators and their parameters, which leads to a large configuration space of pipeline structures and parameterizations. This configuration space has to be explored efficiently in order to find a solution that fulfills the specific requirements of the target application. In this paper, we present an approach to perform automatic configuration based on structure templates and sequential model-based optimization. The structure templates allow to reduce the search space and encode prior engineering knowledge. We introduce a structure template based on hypothesis generation, hypothesis refinement, and hypothesis testing to demonstrate the effectiveness of the approach. Experimental evaluation with state-of-the-art operators is performed on data from the T-LESS dataset as well as in a real-world robotic assembly task. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|