Real-time object detection and pose estimation using stereo vision. An application for a Quadrotor MAV

Zhou, Shuting; Flores, Gerardo; Bazan, Eric; Lozano, Rogelio; Rodrı́guez, Alejandro

doi:10.1109/red-uas.2015.7440992

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…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. [25] utilizes stereo image pairs for detecting and estimating a window that can potentially be used by an MAV for entering a building. However, the proposed algorithm was verified only on data captured using a hand-held stereo rig.…”

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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“…In this work, we address the problem of identifying free navigation areas instead of detecting a particular obstacle. We have chosen such an approach due to the high complexity in determining a broad class of objects when we deal with object classifier approach [18], [19]. For that aim, we use the information of two lowcost devices: a stereo camera rig and a 1D-LiDAR.…”

Section: B Main Contributionmentioning

confidence: 99%

Depth map estimation methodology for detecting free-obstacle navigation areas

Trejo

Martinez

Flores

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a vision-based methodology which makes use of a stereo camera rig and a one dimension LiDAR to estimate free obstacle areas for quadrotor navigation. The presented approach fuses information provided by a depth map from a stereo camera rig, and the sensing distance of the 1D-LiDAR. Once the depth map is filtered with a Weighted Least Squares filter (WLS), the information is fused through a Kalman filter algorithm. To determine if there is a free space large enough for the quadrotor to pass through, our approach marks an area inside the disparity map by using the Kalman Filter output information. The whole process is implemented in an embedded computer Jetson TX2 and coded in the Robotic Operating System (ROS). Experiments demonstrate the effectiveness of our approach.

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“…In-home environments, the GPS signal lacks precision or is unrecognizable (Granillo and Beltran, 2018;Zhou et al, 2015). Moreover, laser sensors such as LIDARs, which are bulky and power-consuming, are only suitable for heavyload MAVs that support the battery weight (Prophet et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, for in-home applications, a vision-based solution is a feasible alternative that does not require any other accessory devices. As an example, Zhou et al (2015) shows how to navigate a MAV through open windows by using features cascade classifier for stereo images. Other vison-based navigation and localization UAV applications are discussed in Al-Kaff et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Void detection for UAV based on optical flow and vanishing points

Oliveira

Sousa

Morais

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Indoor micro aerial vehicle (MAV) navigation is mostly based on high-computational cost obstacle detection algorithms. In this paper, we propose a path planning framework based on perspective cues. The vanishing points were detected by using the Hough-Canny transform. Due to the cluttering, multiple vanishing points candidates are arranged according to the proximity to the optical flow focus of expansion. Obstacles' depths are detected via the optical flow vector clustering. When multiple planes are recognized, the elected vanishing point is considered the center of the furthest empty plane from the camera. This remark guides the direction of the MAV to a particularly free-of-obstacles void area. Preliminary experimental results indicate that the proposed method is faster than other visual-based navigation algorithms.

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Real-time object detection and pose estimation using stereo vision. An application for a Quadrotor MAV

Cited by 12 publications

References 8 publications

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

Depth map estimation methodology for detecting free-obstacle navigation areas

Void detection for UAV based on optical flow and vanishing points

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