Combined laser and vision-aided inertial navigation for an indoor unmanned aerial vehicle

Magree, Daniel; Johnson, Eric N.

doi:10.1109/acc.2014.6858995

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…Table I summarizes related research on indoor drone localization, with [17], [18] reviewed in [23] and [15], [16] covered in [24]. Our review shows a maximum of 3 different types of technologies for sensor fusion are considered, and only in a few cases is the impact of individual sensors investigated.…”

Section: Related Workmentioning

confidence: 99%

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Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

Gerwen

Geebelen

Wan

et al. 2022

IEEE Trans. Veh. Technol.

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Indoor drone or Unmanned Aerial Vehicle (UAV) operations, automated or with pilot control, are an upcoming and exciting subset of drone use cases. Automated indoor flights tighten the requirements of stability and localization accuracy in comparison with the classic outdoor use cases which rely primarily on (RTK) GNSS for localization. In this paper the effect of multiple sensors on 3D indoor position accuracy is investigated using the flexible sensor fusion platform OASE. This evaluation is based on real-life drone flights in an industrial lab with mmaccurate ground truth measurements provided by motion capture cameras, allowing the evaluation of the sensors based on their deviation from the ground truth in 2D and 3D. The sensors under consideration for this research are: IMU, sonar, SLAM camera, ArUco markers and Ultra-Wideband (UWB) positioning with up to 6 anchors. The paper demonstrates that using this setup, the achievable 2D (3D) indoor localization error varies between 4.4 cm and 21 cm (4.9 cm and 67.2 cm) depending on the selected set of sensors. Furthermore, cost/accuracy tradeoffs are included to indicate the relative importance of different sensor combinations depending on the (engineering) budget and use case. These lab results were validated in a Proof of Concept deployment of an inventory scanning drone with more than 10 flight hours in a 65 000 m 2 warehouse. By combining lab results and real-life deployment experiences, different subsets of sensors are presented as a minimal viable solution for three different indoor use cases considering accuracy and cost: a large drone with little weight-and cost restrictions, one or more medium sized drones, and a swarm of weight and cost restricted nano drones.

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Section: Related Workmentioning

confidence: 99%

“…13 cm 2D / 26 cm 3D 40 Hz no no [16] UWB, RGB-D camera ≤22 cm (x, y or z) not reported camera-only (≤46 cm) UWBonly (≤22 cm) no [17] IMU, laser SLAM, visual SLAM 30 cm 2D 10-25 Hz no no [18] IMU, RGB-D camera (Kinect) 8 cm x-axis, 2D/3D not reported 30 Hz no no real-life drone flights in an industrial lab environment. Fig.…”

mentioning

confidence: 99%

Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

Gerwen

Geebelen

Wan

et al. 2022

IEEE Trans. Veh. Technol.

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“…Lynen et al (2013) presented a general purpose multi-sensor fusion extended Kalman filter (MSF-EKF) that can handle different types of delayed measurement signals from multiple sensors, and provides a more accurate attitude estimation for UAV control and navigation. Magree and Johnson (2014) proposed an integrated navigation system, which combines both visual SLAM and laser SLAM with an EKF-based inertial navigation system. The monocular visual SLAM finds data association and estimates the state of UAVs, while the laser SLAM system performs scan-to-map matching under a Monte Carlo framework.…”

Section: Multi-sensor Fusionmentioning

confidence: 99%

A survey on vision-based UAV navigation

Xue

Xia

et al. 2018

Geo-spatial Information Science

323

122

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Research on unmanned aerial vehicles (UAV) has been increasingly popular in the past decades, and UAVs have been widely used in industrial inspection, remote sensing for mapping & surveying, rescuing, and so on. Nevertheless, the limited autonomous navigation capability severely hampers the application of UAVs in complex environments, such as GPS-denied areas. Previously, researchers mainly focused on the use of laser or radar sensors for UAV navigation. With the rapid development of computer vision, vision-based methods, which utilize cheaper and more flexible visual sensors, have shown great advantages in the field of UAV navigation. The purpose of this article is to present a comprehensive literature review of the vision-based methods for UAV navigation. Specifically on visual localization and mapping, obstacle avoidance and path planning, which compose the essential parts of visual navigation. Furthermore, throughout this article, we will have an insight into the prospect of the UAV navigation and the challenges to be faced.

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“…In [61] a cooperative laser and visual SLAM approach for an UAV that depends solely on a laser, a camera and the inertial sensor has been proposed. The characteristic of the vision subsystem was the correlation of the detected features with the vehicle state and the fact that the detected point database was updated in every loop by an EKF.…”

Section: Visual Localization and Mappingmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

295

120

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During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

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Combined laser and vision-aided inertial navigation for an indoor unmanned aerial vehicle

Cited by 18 publications

References 14 publications

Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

A survey on vision-based UAV navigation

Survey on Computer Vision for UAVs: Current Developments and Trends

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