FH Technikum Wien: Artificial Neural Networks Based State Transition Modeling and Place Categorization

EasyChair Preprint 5223

Abstract

To aid the high-level path-planning decisions of a mobile robot, it has to know not just where it is, but also to identify the type and specifics of that place. Customarily, a deep-learning model such as a convolutional neural network (CNN) is trained to classify the location from a video stream. However, a neural network requires fine-tuning and is limited by the closed-set constraint. Following an extensive research review, the aim was to fine-tune an existing system and extend the set of classes it was trained on. The CNN used for feature extraction has been augmented with machine learning (ML) models which have extended the classification and helped to overcome uncertainties from images showing features of multiple classes. The augmented system outperforms the neural network by correctly classifying 90% of images instead of only 78%.

Keyphrases: Place Categorization, Support Vector Machines, machine learning, mobile robotics, neural networks

@booklet{EasyChair:5223,
  author    = {Andreas Kriegler},
  title     = {FH Technikum Wien: Artificial Neural Networks Based State Transition Modeling and Place Categorization},
  howpublished = {EasyChair Preprint 5223},
  year      = {EasyChair, 2021}}