Citation: Li, Z.; Zhang, Y.
Constrained ESKF for UAV
Positioning in Indoor Corridor
Environment Based on IMU and
WiFi. Sensors 2022, 22, 391. https://
doi.org/10.3390/s22010391
Academic Editor: Arturo
Sanchez-Azofeifa
Received: 4 November 2021
Accepted: 31 December 2021
Published: 5 January 2022
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Article
Constrained ESKF for UAV Positioning in Indoor Corridor
Environment Based on IMU and WiFi
Zhonghan Li
1
and Yongbo Zhang
1,2,
*
1
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China;
Jason_lzh@buaa.edu.cn
2
Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University,
Ningbo 315100, China
* Correspondence: zhangyongbo@buaa.edu.cn
Abstract:
The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current
research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and
complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected
because of complex indoor spatial features, which make it impossible to achieve positioning and
navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment
positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K
Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On
the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are
fused together and realize higher positioning accuracy. The probability-based optimization method
is used for further accuracy improvement. After data fusion, the positioning accuracy increased by
51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm.
After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data
fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost
sensors) are very capable of obtaining high indoor positioning accuracy.
Keywords: UAV; ESKF; WiFi; data fusion; indoor positioning
1. Introduction
An unmanned aerial vehicle (UAV) is a drone that integrates various sensors, flight
control systems, data processing systems, power systems, and other modules, which can
autonomously complete specified tasks without human intervention. Since the application
of UAVs from their initial military purpose, such as Predator, Global Hawk, and Black
Hornet, to later civilian and commercialization purposes, such as DJI and Zero-Zero
technology, the drone market has entered a fierce development state. Rotor-wing UAVs,
especially quadcopters, have good development prospects in indoor environments, due
to their compact structure, easy hovering, and convenient side-flight. However, GNSS
signals cannot be received indoors. Because of topological structures and spatial features,
the indoor environment is complex, and signals are easily blocked and reflected. Therefore,
quadcopters cannot rely on GNSS to realize positioning and navigation indoors. However,
pseudolite-based methods can also solve GNSS-based method problems in an indoor
environment [
1
,
2
]. Considering that current indoor environments can easily receive external
source signals, such as WiFi and Bluetooth, deploying pseudolite transmitters also costs a
lot, and method-based pseudolites may face near–far problems and time synchronization
problems [1], this article mainly discusses other indoor positioning methods.
At present, the navigation and positioning methods used in indoor quadcopters mainly
include the following two categories: vision or LiDAR-based methods and external source-
based methods, in which the external source includes Bluetooth, WiFi, and UWB. Among
them, vision or LiDAR-based methods are the current research hotspot [
3
–
10
], and the
Sensors 2022, 22, 391. https://doi.org/10.3390/s22010391 https://www.mdpi.com/journal/sensors
