Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments

Marković, Lovro; Kovac, Marin; Milijas, Robert; Car, Marko; Bogdan, Stjepan

doi:10.1109/icuas54217.2022.9836124

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…Although mobile LiDAR technology has been applied in underground spatial information measurement, there are still challenges for large-scale 3D data acquisition in long tunnels and indoor spaces without GNSS signals due to the lack of significant features or complex environments. The current research on the rapid acquisition and mapping of underground space information can be divided into four categories: mobile measurement system for measuring and its improvement [11][12][13][14][15][16][17][18][19][20][21], laser based on simultaneous localization and mapping (SLAM) algorithm [22][23][24][25], monocular/ binocular vision sensor [26], and RGBD (Red, Green, Blue, Depth map) vision sensor mobile robot system [27][28][29][30] and its corresponding unmanned aerial vehicle (UAV) [31][32][33][34][35] platform.…”

Section: Introductionmentioning

confidence: 99%

Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Liu

Bao-shan

et al. 2022

Journal of Sensors

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In the underground environment, it is difficult to obtain spatial three-dimensional data because of occlusion and its complexity. Mobile light detection and ranging (LiDAR) measurement technology has the ability to obtain three-dimensional spatial information quickly and accurately, but in the underground environment, because of the lack of global navigation satellite system (GNSS) signal in the integrated navigation system, the measurement accuracy decreases with the increase of time. In this paper, an extended Kalman filter-based loose mode is constructed using the real coordinates of the center of gravity of the target and the measurement information of the laser foothold. It provides an additional space-time reference for the integrated navigation system of the mobile LiDAR system and restricts the errors. The simulation results show that the proposed method can effectively improve the measurement accuracy of mobile LiDAR system and enhance the 3D spatial perception ability of underground space.

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Section: Introductionmentioning

confidence: 99%

Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Liu

Bao-shan

et al. 2022

Journal of Sensors

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“…Comparing the ESEKF to the EFK, the results demonstrate that the ESEKF exhibits a slight better performance compared to the traditional EKF and therefore it is a more desirable option for the purposes of aerial flight movement and manipulation. [12][13][14][15] The design of the ESEKF also allows for ease of quaternion manipulation, numerical stability, and tuning of measurement noise covariance. This section will go into the details of the system and state kinematics of the position estimation.…”

Section: Position Estimation Using Error-state Extended Kalman Filteringmentioning

confidence: 99%

GPS-denied navigation using location estimation and texel image correction

Jensen

Budge

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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This paper presents a localization system for Unmanned Aerial Vehicles (UAVs), specifically designed for small UAVs to be used in a GPS-denied local area by using estimation algorithms incorporating camera and Light Detection and Ranging (LiDAR) sensor fusion. Localization techniques classically rely on Global Positioning System (GPS) information. However, GPS is subject to jamming. This paper proposes methods of localization without reliance on the GPS. This system utilizes Error-State Extended Kalman Filtering (ESEKF) and methods of camera to LiDAR sensor fusion to correct for error propagations in the aerial vehicle's estimated location.Initial results from the GPS-denied navigation method showed that the location of the sUAV to an average error of 3.2 m was possible using only texel images and velocity measurements from an experimental flight.

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“…Similarly, Marković et al [31] proposed an ESKF-based multi-sensor fusion algorithm for UAV localization in indoor environments, which was able to accurately track the UAV's position using measurements from IMU, LiDAR, visual odometry, and UWB sensors. Mourikis and Roumeliotis [32] introduced the Multi-State Constraint Kalman Filter (MSCKF) as an algorithm for estimating the state of a vision-aided inertial navigation (VINS) system.…”

Section: Related Workmentioning

confidence: 99%

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Kabiri

Cimarelli

Bavle

et al. 2022

Sensors

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Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored.

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Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments

Cited by 20 publications

References 22 publications

Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

GPS-denied navigation using location estimation and texel image correction

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

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