Distance Invariant Sparse Autoencoder for Wireless Signal Strength Mapping

TL;DR

This paper introduces a distance-invariant sparse autoencoder that learns compact, low-impact signal strength maps for wireless localization, enabling efficient and accurate robot positioning in outdoor environments.

Contribution

The paper proposes a novel autoencoder architecture that preserves input-output distances, improving wireless signal mapping for localization with reduced dimensionality.

Findings

Autoencoders effectively compress signal maps while maintaining localization accuracy.

Distance invariance in the autoencoder's latent space enhances robustness of the localization.

Experimental results demonstrate successful outdoor environment mapping using the proposed method.

Abstract

Wireless signal strength based localization can enable robust localization for robots using inexpensive sensors. For this, a location-to-signal-strength map has to be learned for each access point in the environment. Due to the ubiquity of Wireless networks in most environments, this can result in tens or hundreds of maps. To reduce the dimensionality of this problem, we employ autoencoders, which are a popular unsupervised approach for feature extraction and data compression. In particular, we propose the use of sparse autoencoders that learn latent spaces that preserve the relative distance between inputs. Distance invariance between input and latent spaces allows our system to successfully learn compact representations that allow precise data reconstruction but also have a low impact on localization performance when using maps from the latent space rather than the input space. We demonstrate the feasibility of our approach by performing experiments in outdoor environments.