IEEE Internet of Things Journal · 2023 · 41 citations · 25 references
ComputingAutonomous SystemsComputer ScienceComputer VisionMachine VisionSensor FusionDepth EstimationMotion CaptureOptical FlowRetinal DisparityStereo VisionDisparity ComputationDistance EstimationFlightMultiscale AnalysisMultiscale Scene AnalysisOptical Flow EstimationVelocity Field MeasurementVelocity Measurement
Autonomous velocity measurement technology based on optical flow plays an important role in applications of the Internet of Things. However, robust velocity measurement results need to provide a robust optical flow field and distance to the ground, especially for complex ground scenes. To solve this problem, this article proposes a visual velocity measurement method based on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> camera-array multiaperture biomimetic compound-eye imaging system. The multiaperture optical flow field is first obtained from compound-eye through multiscale analysis and Bayes threshold processing. The real distance to the ground is then obtained by extracting the disparity information of nine apertures for adaptive depth estimation. The velocity measurement error is less than 0.6 m/s in low-altitude (8 and 13 m) flight scenarios with multiple obstacles.
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A Database and Evaluation Methodology for Optical Flow
Simon Baker, Daniel Scharstein, John Lewis et al. · International Journal of Computer Vision · 2010 · 2.2K citations · Full text
Object scene flow for autonomous vehicles
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