DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE¹ - Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as real-world videos acquired through multiple mobile platforms. The final results show an improvement over the existing state-of-the-art visual place recognition systems.

I. Introduction

Scene recognition and understanding has been an important area of research in the robotics and computer vision community for more than a decade now. Programming robots to identify their surroundings is integral to building autonomous systems for aiding humans in house-hold environments.