Resource-Aware Programming for Robotic Vision

TL;DR

This paper explores resource-aware programming for humanoid robots, demonstrating how Invasive Computing enhances adaptability and result quality in computer vision tasks on multi-core architectures.

Contribution

It introduces and evaluates Invasive Computing as a resource-aware paradigm for improving computer vision performance in robotic systems.

Findings

Invasive Computing improves adaptability of vision algorithms.

Enhanced resource management leads to better result quality.

The methodology is effective on multi-core architectures.

Abstract

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.