Unsupervised Place Discovery for Place-Specific Change Classifier

TL;DR

This paper introduces an unsupervised method for discovering places in robotic environments to improve the training of place-specific change classifiers, using visual recognition techniques, and demonstrates its effectiveness on change detection tasks.

Contribution

The paper proposes a novel unsupervised approach for partitioning robot workspace into places to enhance change classifier training, addressing a key challenge in robotic map learning.

Findings

Effective unsupervised place discovery method demonstrated

Improved change classifier performance on real datasets

Applicable to nuisance and anomaly detection

Abstract

In this study, we address the problem of supervised change detection for robotic map learning applications, in which the aim is to train a place-specific change classifier (e.g., support vector machine (SVM)) to predict changes from a robot's view image. An open question is the manner in which to partition a robot's workspace into places (e.g., SVMs) to maximize the overall performance of change classifiers. This is a chicken-or-egg problem: if we have a well-trained change classifier, partitioning the robot's workspace into places is rather easy. However, training a change classifier requires a set of place-specific training data. In this study, we address this novel problem, which we term unsupervised place discovery. In addition, we present a solution powered by convolutional-feature-based visual place recognition, and validate our approach by applying it to two place-specific change classifiers, namely, nuisance and anomaly predictors.