Robust Topological Feature Extraction for Mapping of Environments using Bio-Inspired Sensor Networks

TL;DR

This paper presents a novel method for environment mapping using biologically inspired sensor networks, leveraging topological data analysis and robust classification to improve accuracy with minimal sensing information.

Contribution

It introduces a new approach combining probabilistic motion models, topological data analysis, and robust classification for environment mapping with bio-inspired sensors.

Findings

Effective topological features extracted from minimal sensing data.

Robustness improved through density-based subsampling and scale-invariant classification.

Enhanced mapping accuracy with encounter metrics based on agent motion information.

Abstract

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter information in order to build a topological representation of the environment. Neighbor to neighbor interactions among the nodes are exploited to build point clouds representing spatial features of the manifold characterizing the environment based on the sampled data. To extract dominant features from sampled data, topological data analysis is used to produce persistence intervals for features, to be used for topological mapping. In order to improve robustness characteristics of the sampled data with respect to outliers, density based subsampling algorithms are employed. Moreover, a robust scale-invariant classification algorithm for persistence diagrams is proposed to provide a quantitative representation of desired features in the data. Furthermore, various strategies for defining encounter metrics with different degrees of information regarding agents' motion are suggested to enhance the precision of the estimation and classification performance of the topological method.