Image processing using Pearson's correlation coefficient: Applications on autonomous robotics

Autonomous robots have motivated researchers from different groups due to the challenge that it represents. 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, we have proposed a set of tools based on Pearson's Correlation Coefficient (PCC): (i) a Discarding Criteria methodology was proposed and applied as (ii) a Dynamic Power Management solution; (iii) an environment observer method based on PCC selects automatically only the Regions-Of-Interest; and taking place in the obstacle avoidance context, (iv) a method for Collision Risk Estimation was proposed for vehicles in dynamic and unknown environments. Applying the PCC to these tasks has not been done yet, making the concepts unique. All these solutions have been evaluated from real data obtained by experimental vehicles.