Adaptive visual odometry using RGB-D cameras

Fabian, Joshua

doi:10.1109/aim.2014.6878301

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…As the main challenges of using monocular cameras in a mounting structure, image scale Drones 2021, 5, 121 2 of 14 uncertainty [10] and motion blur [11] in images are observed. In recent studies, resolution of such troubles has been attempted by using stereo [12,13] and RGB-D [14][15][16] cameras and visual inertial SLAM (viSLAM) methods. However, such types of cameras have disadvantages in terms of size, weight and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

Şimşek

Bilge

2021

Drones

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Localization and mapping technologies are of great importance for all varieties of Unmanned Aerial Vehicles (UAVs) to perform their operations. In the near future, it is planned to increase the use of micro/nano-size UAVs. Such vehicles are sometimes expendable platforms, and reuse may not be possible. Compact, mounted and low-cost cameras are preferred in these UAVs due to weight, cost and size limitations. Visual simultaneous localization and mapping (vSLAM) methods are used for providing situational awareness of micro/nano-size UAVs. Fast rotational movements that occur during flight with gimbal-free, mounted cameras cause motion blur. Above a certain level of motion blur, tracking losses exist, which causes vSLAM algorithms not to operate effectively. In this study, a novel vSLAM framework is proposed that prevents the occurrence of tracking losses in micro/nano-UAVs due to the motion blur. In the proposed framework, the blur level of the frames obtained from the platform camera is determined and the frames whose focus measure score is below the threshold are restored by specific motion-deblurring methods. The major reasons of tracking losses have been analyzed with experimental studies, and vSLAM algorithms have been made durable by our studied framework. It has been observed that our framework can prevent tracking losses at 5, 10 and 20 fps processing speeds. vSLAM algorithms continue to normal operations at those processing speeds that have not been succeeded before using standard vSLAM algorithms, which can be considered as a superiority of our study.

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Section: Introductionmentioning

confidence: 99%

A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

Şimşek

Bilge

2021

Drones

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Review of visual odometry: types, approaches, challenges, and applications

et al. 2016

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Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time. Vision-based odometry is a robust technique utilized for this purpose. It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle. This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges. VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors. Several areas for future research are also highlighted.

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Adaptive visual odometry using RGB-D cameras

Cited by 2 publications

References 15 publications

A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

Review of visual odometry: types, approaches, challenges, and applications

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