Learning-based distance evaluation in robot vision: A comparison of ANFIS, MLP, SVR and bilinear interpolation models

This paper deals with visual evaluation of object distances using Soft-Computing based approaches and pseudo-3D standard low-cost sensor, namely the Kinect. The investigated technique points toward robots' vision and visual metrology of the robot's surrounding environment. The objective is providing the robot the ability of evaluating distances between objects in its surrounding environment. In fact, although presenting appealing advantages, the Kinect has not been designed for metrological aims. The investigated approach offers the possibility to use this low-cost pseudo-3D sensor for distance evaluation avoiding 3D feature extraction and thus exploiting the simplicity of only 2D image' processing. Experimental results show the viability of the proposed approach and provide comparison between different machine learning techniques as Adaptive-network-based fuzzy inference (ANFIS), Multi-layer Perceptron (MLP), Support vector regression (SVR), Bilinear interpolation.