Video-based navigation (VBN) is increasingly used in space applications to enable autonomous entry, descent, and landing of aircrafts. VBN algorithms require real-time performances and high computational capabilities, especially to perform features extraction and matching (FEM). In this context, field-programmable gate arrays (FPGAs) can be employed as efficient hardware accelerators. This paper proposes an improved FPGA-based FEM module. Online self-adaptation of the parameters of both the image noise filter and the features extraction algorithm is adopted to improve the algorithm robustness. Experimental results demonstrate the effectiveness of the proposed self-adaptive module. It introduces a marginal resource overhead and no timing performance degradation when compared with the reference state-of-the-art architecture.
|Titolo:||SA-FEMIP: A Self-Adaptive Features Extractor and Matcher IP-Core Based on Partially Reconfigurable FPGAs for Space Applications|
|Data di pubblicazione:||2015|
|Digital Object Identifier (DOI):||10.1109/TVLSI.2014.2357181|
|Appare nelle tipologie:||1.1 Articolo in rivista|