A laser and image based navigation and guidance system for autonomous outdoor-indoor transition flights of MAVs

Popp, Manuel; Scholz, Georg; Prophet, Silvia; Trommer, Gert F.

doi:10.1109/inertialsensors.2015.7314267

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…However, their approach for window detection requires an operator to manually select a point of interest in the RGB image, and was verified only by simulations. This approach was further extended and verified in real-world experiments with flight through a wooden frame in [24]. However, the experimental verification only consisted of manually guiding the MAV in front of the window, autonomous flight through the window, and immediate manual landing.…”

Section: B Related Work and Contributionmentioning

confidence: 99%

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

et al. 2021

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This paper presents a novel approach to autonomous extinguishing of indoor fires inside a building by a Micro-scale Unmanned Aerial Vehicle (MAV). In particular, controlling and estimating the MAV state, detection of a building entrance, multi-modal MAV localization during the outdoorindoor transition, interior motion planning and exploration, fire detection and position estimation, and fire extinguishing are discussed. The performance of these elements, as well as of the entire integrated system, are evaluated in simulations and field tests in various demanding real-world conditions. The system presented here is part of a complex multi-MAV solution that won the Mohamed Bin Zayed International Robotics Challenge 2020 (MBZIRC 2020) competition, and is being used as the core of a fire-fighting Unmanned Aerial System (UAS) industrial platform under development. A video attachment to this paper is available at the website http://mrs.felk.cvut.cz/2020firechallenge-insidefires.

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Section: B Related Work and Contributionmentioning

confidence: 99%

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

et al. 2021

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“…The master filter and local filters use the 18th state variables composed of the errors of latitude, longitude, altitude, velocity and attitude, accelerometer bias, gyro bias, barometer error compensation acceleration, barometer bias, and barometer scale factor. If both TRN and GNSS are not available, as in Case 4, the master filter only performs time propagation in equations (5) and (6). If there is any available local filter, the master filter performs not only the time propagation but also the appropriate measurement update for each case.…”

Section: Design Of the Master Filter Based On The Federated Filtermentioning

confidence: 99%

“…However, the GNSS cannot operate independently as it provides information with outside help and is vulnerable to hostile jamming and spoofing. To overcome such weaknesses, the vision-based navigation technique is commonly used for UAVs [3]- [5], but a problem with the camera adapting to the changing conditions of a real flight is not completely solved yet, so vision-based navigation is mainly used in drone systems that fly at low altitude for a relatively short time [6] and autonomous driving vehicles [7]. As another technique that can resolve the problems of GNSS, the terrain referenced navigation (TRN) can be used.…”

Section: Introductionmentioning

confidence: 99%

A Pragmatic Approach to the Design of Advanced Precision Terrain Aided Navigation for UAVs and Its Verification

Lee¹,

Sung²,

Oh³

et al. 2020

Preprint

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Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

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“…In order to determine an accurate and precise nav igation solution for both indoor and outdoor areas, the navigation filter presented in [15] is used for the out door indoor transition flights. Briefly summarized, an integrated navigation system is additionally corrected with delta pose measurements, provided by a laser rangefinder's scan matching and image based feature matching.…”

Section: Navigation Filtermentioning

confidence: 99%