Processing chain for 3D histogram of gradients based real-time object recognition. (9th February 2021)

• Record Type: Journal Article
• Authors: Vilar, Cristian; Krug, Silvia; Thörnberg, Benny
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 1(2021)
• Publication Date: 2021-02-09
• Subjects: 3D object recognition -- 3D image processing -- histogram of oriented gradients -- handcrafted descriptor -- embedded systems -- machine learning; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420978363
• Languages: English
• ISSNs: 1729-8806

Abstract

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh ) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.