Fast Feature Matching in Visual-Inertial SLAM

Lin, Feng; Qu, Xinyi; Ye, Xuetong; Wang, Kang; Li, Xueyuan

doi:10.1109/icarcv57592.2022.10004220

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…With the improvement of the accuracy of laser gyro, fiber-optic gyro, and micro-electromechanical system (MEMS) inertial guidance technology, as well as the decrease in the cost and volume, inertial guidance has been employed in a large number of applications for the solution of position and navigation under multi-sensors. For example, inertial guidance is usually combined with a global navigation satellite system (GNSS) for integrated navigation [ 26 , 27 , 28 ], with a Doppler velocity log (DVL) for underwater navigation [ 29 ], and with a radar or camera for simultaneous localization and mapping (SLAM) algorithms [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

An Autocollimator Axial Measurement Method Based on the Strapdown Inertial Navigation System

Ma,

Li,

Liu

et al. 2024

Sensors

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Autocollimators are widely used optical axis-measuring tools, but their measurement errors increase significantly when measuring under non-leveled conditions and they have a limited measurement range due to the limitations of the measurement principle. To realize axis measurement under non-leveled conditions, this paper proposes an autocollimator axis measurement method based on the strapdown inertial navigation system (SINS). First, the measurement model of the system was established. This model applies the SINS to measure the change in attitude of the autocollimator. The autocollimator was then applied to measure the angular relationship between the measured axis and its own axis, based on which the angular relationship of the axis was measured via computation through signal processing and data fusion in a multi-sensor system. After analyzing the measurement errors of the system model, the Monte Carlo method was applied to carry out a simulation analysis. This showed that the majority of the measurement errors were within ±0.002° and the overall measurement accuracy was within ±0.006°. Tests using equipment with the same parameters as those used in the simulation analysis showed that the majority of the measurement errors were within ±0.004° and the overall error was within ±0.006°, which is consistent with the simulation results. This analysis proves that this method solves the problem of the autocollimator being unable to measure the axis under non-leveled conditions and meets the needs of axis measurement with the application of autocollimators under a moving base.

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Section: Introductionmentioning

confidence: 99%

An Autocollimator Axial Measurement Method Based on the Strapdown Inertial Navigation System

Ma,

Li,

Liu

et al. 2024

Sensors

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Detecting and Mitigating Attacks on GPS Devices

Burbank,

Greene,

Kaabouch

2024

Sensors

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Modern systems and devices, including unmanned aerial systems (UASs), autonomous vehicles, and other unmanned and autonomous systems, commonly rely on the Global Positioning System (GPS) for positioning, navigation, and timing (PNT). Cellular mobile devices rely on GPS for PNT and location-based services. Many of these systems cannot function correctly without GPS; however, GPS signals are susceptible to a wide variety of signal-related disruptions and cyberattacks. GPS threat detection and mitigation have received significant attention recently. There are many surveys and systematic reviews in the literature related to GPS security; however, many existing reviews only briefly discuss GPS security within a larger discussion of cybersecurity. Other reviews focus on niche topics related to GPS security. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security, with an emphasis on UAS applications. This paper provides an overview of the threats to GPS and the state-of-the-art techniques for attack detection and countermeasures. Detection and mitigation approaches are categorized, and the strengths and weaknesses of existing approaches are identified. This paper also provides a comprehensive overview of the state-of-the-art on alternative positioning and navigation techniques in GPS-disrupted environments, discussing the strengths and weaknesses of existing approaches. Finally, this paper identifies gaps in existing research and future research directions.

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Fast Feature Matching in Visual-Inertial SLAM

Cited by 2 publications

References 15 publications

An Autocollimator Axial Measurement Method Based on the Strapdown Inertial Navigation System

An Autocollimator Axial Measurement Method Based on the Strapdown Inertial Navigation System

Detecting and Mitigating Attacks on GPS Devices

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