Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot

TL;DR

This paper evaluates three vision-based object perception methods for mobile robots, focusing on their effectiveness and challenges in unstructured indoor environments, aiming to develop lightweight solutions suitable for real-time applications.

Contribution

It analyzes the performance issues of three state-of-the-art object recognition methods in mobile robotics and proposes segmentation techniques to improve their applicability.

Findings

Identified key challenges in applying recognition methods in mobile scenarios

Proposed segmentation approaches to enhance detection accuracy

Evaluated methods' performance in typical indoor environments

Abstract

This paper addresses object perception applied to mobile robotics. Being able to perceive semantically meaningful objects in unstructured environments is a key capability in order to make robots suitable to perform high-level tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.