  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.
