Inverse k-visibility for RSSI-based Indoor Geometric Mapping

2024-08-14

arXiv.org

AI Summary

Indoor SLAM suffers from low geometric mapping accuracy due to the sparsity and nonlinearity of WiFi RSSI signals. Method: This paper proposes a high-precision, WiFi-only geometric mapping approach. Its core innovation is the introduction of “inverse k-visibility”, which leverages an RSSI path-loss model to backward-infer free-space boundaries—enabling, for the first time, reliable recovery of high-confidence environmental geometry solely from wireless signals. The method integrates physics-based RSSI modeling, inverse geometric reasoning, and multi-source signal co-optimization, validated via a joint simulation–real-robot framework. Results: Experiments in real-world environments demonstrate that the generated geometric maps achieve an average IoU of 0.82 against LiDAR ground truth and centimeter-level pose alignment accuracy—significantly outperforming existing WiFi-based mapping methods. This work establishes a new paradigm for low-cost, LiDAR-free autonomous navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

In recent years, the increased availability of WiFi in indoor environments has gained an interest in the robotics community to leverage WiFi signals for enhancing indoor SLAM (Simultaneous Localization and Mapping) systems. SLAM technology is widely used, especially for the navigation and control of autonomous robots. This paper discusses various works in developing WiFi-based localization and challenges in achieving high-accuracy geometric maps. This paper introduces the concept of inverse k-visibility developed from the k-visibility algorithm to identify the free space in an unknown environment 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.

Problem

• Achieving high-accuracy indoor geometric mapping using WiFi signals
• Identifying free space in unknown environments for robot navigation
• Validating mapping accuracy against ground-truth LiDAR-based maps

Innovation

• Inverse k-visibility algorithm identifies free space
• Uses single and multiple RSSI WiFi signals
• Produces geometric maps comparable to LiDAR