Pearson's correlation coefficient: a more realistic threshold for applications on autonomous robotics

• Autor(a) principal: Miranda Neto, Arthur de
• Data de Publicação: 2014
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/15228
• topic: Pearson’s correlation; Mobile robots; Autonomous robotics; Correlação de Pearson; Robôs móveis; Robótica autónoma

Many applications for control of autonomous platform are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing. Taking into account the temporal coherence between consecutive frames, the PCC (Pearson’s Correlation Coefficient) was proposed and applied as: discarding criteria methodology, dynamic power management solution, environment observer method which selects automatically only the regions-of-interest; and taking place in the obstacle avoidance context, as a method for collision risk estimation for vehicles in dynamic and unknown environments. Even if the PCC is a great tool to help the autonomous or semi-autonomous navigation, distortions in the imaging system, pixel noise, slight variations in the object’s position relative to the camera, and other factors produce a false PCC threshold. Whereas there are homogeneous regions in the image, in order to obtain a more realistic Pearson’s correlation, we propose to use some prior known environment information.