Factor Graph-Based Collaborative Localization and Incremental UWB Network Deployment in Indoor Environments for Emergency Task Forces

Eva Buchmayer, Axel Koppert, Fabian Theurl, and Markus Watzko

Peer Reviewed

Abstract:	This paper presents a human-machine teaming approach for localization in unknown indoor environments where a mobile robot maps the environment with a LiDAR-based SLAM algorithm and a pedestrian sets up a UWB network. UWB ranges are measured between the robot and the pedestrian, as well as between each moving agent and the UWB anchors. The states of the robot and the pedestrian and the locations of the UWB anchors are jointly estimated using factor graph optimization. This allows gaining a rapid situational overview and provides the basis for coordinating complex operations in urban and indoor environments. The approach is validated using a test dataset. The results show that the UWB anchors can be mapped with a horizontal accuracy of less than 40 cm. The 2D RMS for the robot is less than 40 cm and the 2D RMS of the pedestrian is less than 1 m. Index Terms—Factor graph optimization, collaborative navigation, human-machine teaming, UWB, indoor navigation, SLAM
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	1429 - 1438
Cite this article:	Buchmayer, Eva, Koppert, Axel, Theurl, Fabian, Watzko, Markus, "Factor Graph-Based Collaborative Localization and Incremental UWB Network Deployment in Indoor Environments for Emergency Task Forces," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 1429-1438.
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