Generic Object Discrimination for Mobile Assistive Robots Using Projective Light Diffusion

Abstract

A number of assistive robot services depend on the classification of objects while dealing with an increased volume of sensory data, scene variability and limited computational resources. We propose using more concise representations via a seamless combination of photometric and geometric features fused by exploiting local photometric/geometric correlation and employing domain transform filtering in order to recover scene structure. This is obtained through a projective light diffusion imaging process (PLDI) which allows capturing surface orientation, image edges and global depth gradients into a single image. Object candidates are finally encoded into a discriminative, wavelet-based descriptor allowing very fast object queries. Experiments with an indoor robot demonstrate improved classification performance compared to alternative methods and an overall superior discriminative power compared to state-of-the-art unsupervised descriptors within ModelNet10 benchmark.

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

“…Additionally, it can be observed that these algorithms are executed in real time. Regarding hardware characteristics, robotic platforms such as the following robots were used: PR2 [ 105 ], Toyota’s Human Support [ 107 ], Romeo 2 [ 106 ], Turtlebot 2 [ 98 ], and NAO [ 108 ].…”

“…This information was used for navigation planning. Additionally, Papadakis et al in [ 106 ] proposed the projective light diffusion imaging process (PLDI) algorithm for the ROMEO2 robot capable of recognizing domestic objects for navigation planning. The authors implemented joint bilateral filtering to reconstruct the RGB-D images and ignore pixels with invalid depth values.…”