Resource-Aware Programming for Robotic Vision
This work addresses resource management for humanoid robots in dynamic environments, but it is incremental as it applies an existing methodology to specific vision tasks.
The paper tackles the challenge of adapting robotic vision algorithms to future many-core architectures by analyzing the Invasive Computing programming model, showing that it improves adaptability and result quality for Harris Corner detector and SIFT feature matching.
Humanoid robots are designed to operate in human centered environments. They face changing, dynamic environments in which they need to fulfill a multitude of challenging tasks. Such tasks differ in complexity, resource requirements, and execution time. Latest computer architectures of humanoid robots consist of several industrial PCs containing single- or dual-core processors. According to the SIA roadmap for semiconductors, many-core chips with hundreds to thousands of cores are expected to be available in the next decade. Utilizing the full power of a chip with huge amounts of resources requires new computing paradigms and methodologies. In this paper, we analyze a resource-aware computing methodology named Invasive Computing, to address these challenges. The benefits and limitations of the new programming model is analyzed using two widely used computer vision algorithms, the Harris Corner detector and SIFT (Scale Invariant Feature Transform) feature matching. The result indicate that the new programming model together with the extensions within the application layer, makes them highly adaptable; leading to better quality in the results obtained.