Instantaneous Wireless Robotic Node Localization Using Collaborative Direction of Arrival

TL;DR

This paper introduces an online wireless robot localization method using collaborative signals and geometric analysis, achieving high accuracy and real-time performance in GPS-denied environments.

Contribution

The paper presents a novel collaborative Direction of Arrival technique combined with EM and Particle Filter algorithms for resource-efficient, high-precision robot localization.

Findings

Achieves centimeter-level localization accuracy.

Operates effectively with RSSI-only data.

Demonstrates real-time performance in diverse environments.

Abstract

Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprinting and feature-matching paradigms, requiring dedicated environment-specific offline data collection. We propose an online robot localization algorithm enabled by collaborative wireless sensor nodes to remedy these limitations. Our approach's core novelty lies in obtaining the Collaborative Direction of Arrival (CDOA) of wireless signals by exploiting the geometric features and collaboration between wireless nodes. The CDOA is combined with the Expectation Maximization (EM) and Particle Filter (PF) algorithms to calculate the Gaussian probability of the node's location with high efficiency and accuracy. The algorithm relies on RSSI-only data, making it ubiquitous to resource-constrained devices. We theoretically analyze the approach and extensively validate the proposed method's consistency, accuracy, and computational efficiency in simulations, real-world public datasets, as well as real robot demonstrations. The results validate the method's real-time computational capability and demonstrate considerably-high centimeter-level localization accuracy, outperforming relevant state-of-the-art localization approaches.