Guidance: A visual sensing platform for robotic applications

Zhou, Guyue; Tang, Ketan; Zhang, Honghui; Wang, Kai; Yang, Kang

doi:10.1109/cvprw.2015.7301360

Cited by 35 publications

(16 citation statements)

References 27 publications

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“…For the onboard visual sensing system, five units of low resolution stereo camera and one processor named Guidance [20] are implemented. Guidance is compatible with the flight controller of the aerial platform, and it fuses stereo camera data for real-time obstacle avoidance in this work.…”

Section: Hardwarementioning

confidence: 99%

Transfer Learning-Based Crack Detection by Autonomous UAVs

Kucuksubasi¹,

Sorguç²

2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection.

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Section: Hardwarementioning

confidence: 99%

Transfer Learning-Based Crack Detection by Autonomous UAVs

Kucuksubasi¹,

Sorguç²

2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Installing more cameras on the aerial robots and constructing a multicamera system is the most direct approach to build an omnidirectional vision system, but this not only increases weight and cost but also results in tight constraints on the structural design. There exist extensive works focusing on dealing with multiple stereo cameras (Gohl et al, 2015; G. Zhou et al, 2015). Using a particular optical device, such as catadioptric cameras, with two convex reflective mirrors (Gluckman, Nayar, & Thoresz, 1998), with one camera and one convex reflective mirror (W. Li & Li, 2011), or with plane mirrors (F. Zhou, Chai, Chen, & Song, 2016), is economical due to their panoramic view.…”

Section: Introductionmentioning

confidence: 99%

Autonomous aerial robot using dual‐fisheye cameras

Gao

Kaixuan

Ding

et al. 2020

Journal of Field Robotics

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Safety is undoubtedly the most fundamental requirement for any aerial robotic application. It is essential to equip aerial robots with omnidirectional perception coverage to ensure safe navigation in complex environments. In this paper, we present a light-weight and low-cost omnidirectional perception system, which consists of two ultrawide field-of-view (FOV) fisheye cameras and a low-cost inertial measurement unit (IMU). The goal of the system is to achieve spherical omnidirectional sensing coverage with the minimum sensor suite. The two fisheye cameras are mounted rigidly facing upward and downward directions and provide omnidirectional perception coverage: 360°FOV horizontally, 50°FOV vertically for stereo, and whole spherical for monocular. We present a novel optimization-based dualfisheye visual-inertial state estimator to provide highly accurate state-estimation.Real-time omnidirectional three-dimensional (3D) mapping is combined with stereobased depth perception for the horizontal direction and monocular depth perception for upward and downward directions. The omnidirectional perception system is integrated with online trajectory planners to achieve closed-loop, fully autonomous navigation. All computations are done onboard on a heterogeneous computing suite.Extensive experimental results are presented to validate individual modules as well as the overall system in both indoor and outdoor environments.

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“…The M100 is also equipped with the DJI Guidance module, which includes one additional downward facing camera for optical flow measurements and seamlessly integrates with the INS to provide us with position, velocity and acceleration measurements of the M100 using a fusion of on-board sensors described in [17]. This information is used as input to our Kalman filter, see Section III B 1.…”

Section: A System Descriptionmentioning

confidence: 99%

“…Many commercial MAVs are equipped with velocity sensors relying on optical flow methods, which visually estimate velocity by computing the movement of features in successive images, see, e.g., [17].…”

Section: System Architecturementioning

confidence: 99%