Inverse k-visibility for RSSI-based Indoor Geometric Mapping

TL;DR

This paper introduces inverse k-visibility, a novel algorithm leveraging RSSI WiFi signals for high-accuracy indoor geometric mapping, enabling improved navigation and obstacle avoidance in robotics.

Contribution

It presents the inverse k-visibility algorithm, adapting k-visibility for WiFi-based free space detection in indoor environments, validated through extensive experiments.

Findings

Robustness demonstrated in simulated and real-world tests

High correlation with LiDAR-based ground-truth maps

Effective in both single and multiple RSSI signal scenarios

Abstract

In recent years, the increased availability of WiFi in indoor environments has gained interest in the robotics community to utilize WiFi signals for indoor simultaneous localization and mapping algorithms. This paper discusses the challenges of achieving high-accuracy geometric map building using WiFi signals. The paper introduces the concept of inverse k-visibility, developed from the k-visibility algorithm, to identify free space in an unknown environment, used for planning, navigation, and obstacle avoidance. Comprehensive experiments, including those utilizing single and multiple RSSI signals, were conducted in both simulated and real-world environments to demonstrate the robustness of the proposed algorithm. Additionally, a detailed analysis comparing the resulting maps with ground-truth LiDAR-based maps is provided to highlight the algorithm's accuracy and reliability.