Robust Place Categorization with Deep Domain Generalization

TL;DR

This paper introduces a deep learning framework for robot place categorization that generalizes to unseen environments by combining models from known domains, addressing limitations of domain adaptation methods.

Contribution

The paper proposes a novel deep domain generalization approach using a CNN with weighted Batch Normalization layers to improve robustness in unseen scenarios.

Findings

Effective in unseen environments across three datasets

Outperforms existing domain adaptation methods

Demonstrates robustness to environmental changes

Abstract

Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper we present an approach which aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g. corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a Convolutional Neural Network architecture with novel layers performing a weighted version of Batch Normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution.