| Popis: |
The purpose of this work is to explore the design principles for a Real-Time Robotic Multi Camera Vision System, in a case study involving a real world competition of autonomous driving. Design practices from vision and real-time research areas are applied into a Real-Time Robotic Vision application, thus exemplifying good algorithm design practices, the advantages of employing the “zero copy one pass” methodology and associated trade-offs leading to the selection of a controller platform. The vision tasks under study are: (i) recognition of a “flat” signal; and (ii) track following, requiring 3D reconstruction. This research firstly improves the used algorithms for the mentioned tasks and finally selects the controller hardware. Optimization for the shown algorithms yielded from 1.5 times to 190 times improvements, always with acceptable quality for the target application, with algorithm optimization being more important on lower computing power platforms. Results also include a 3-cm and five-degree accuracy for lane tracking and 100% accuracy for signalling panel recognition, which are better than most results found in the literature for this application. Clear results comparing different PC platforms for the mentioned Robotic Vision tasks are also shown, demonstrating trade-offs between accuracy and computing power, leading to the proper choice of control platform. The presented design principles are portable to other applications, where Real-Time constraints exist. |
