2010 · 20 citations · 9 references
ComputingAutonomous SystemsComputer ScienceComputer VisionRobot PerceptionSensor FusionMappingPoint Cloud ProcessingRobot CalibrationRobotic VisionScene ReconstructionUncertainty EstimationFusion Of Heterogeneous SensorsLaser Range FindingMap Consistency VerificationUnmanned Ground Vehicles
This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.
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Autonomous driving in urban environments: Boss and the Urban Challenge
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Junior: The Stanford entry in the Urban Challenge
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A perception‐driven autonomous urban vehicle
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