Variable Resolution Vision System in Mobile Robotics Armando Sousa Armando Sousa Paulo Costa, António Moreira Faculdade de Engenharia da Universidade do Porto (FEUP) Instituto de Sistemas e Robótica R. Dr. Roberto Frias, S/N / Porto / Portugal

Problem: Robot Vision Onboard cameras are the main sensor –Quality Cameras  Large Image –Larger Image    Comp. Power Embedded Robot Vision –Real Time Constraints –Limited Computing Power Available

Vision Problems Given several Vision Problems (a) Camera Onboard (b) Static External Camera (c) generic object moves in 3D over a plane Objects Nearer are Always Larger

Goal Statement Structured Vision –Objects Closer are Larger –Distance to Vision Plane is Known  Not every pixel is essential to find close objects  Sub Sample the Image !!! Projection Effect

Pin-Hole Camera Model Objects far away occupy less pixels

Lens Distortion Model Lens distortion model (barrel / pin-cushion)

Math Results Sub Sample image with density proportional to distance * No lens distortion correction Dens = Min(Distance/Horizon,1)

Distance Graph

Offline Densities Bitmap Distance Graph GeyScale Distance Encoding Dithered Bitmap using Floyd Steinberg error diffusion)

Example Actual images relating to the 5dpo-2000 Robotic Soccer Team

Pixel Weight Error in center of cluster due to Center of mass calculations Taking local densities into consideration (important for tall objects)

Review of Algorithm Generate Offline Densities Bitmap: –distance formulae  pixel densities  grayscale bitmap  B&W Pixel Classification: –black pixels  run pixel color calibration Clustering & Merging: –Cluster neighboring pixels together Iterate Image: –Iterate for the whole image to take advantage of cache optimisations

Tech Compare Chart Results relating to the 5dpo-2000 Robotic Soccer Team (BT878, PAL, 25 fps)

Conclusions Main advantage of the method is that objects far away are more clearly seen The presented method is suited to Real Time applications Method involves re-sampling the image! –interesting if computing power is not enough to process the whole image at maximum resolution Generation of offline Dither Bitmap implies all camera parameters must be known and constant Bitmap densities parameters: –min density  min object size and required precision –Greatly affects execution time

Future Work Adapt the algorithm for moving cameras Measurement's accuracy can be improved by rescanning the boundaries of the objects at full resolution Other considerations for distance model may be taken into consideration