End-to-end multi-task regression learns flight commands for UAVs to explore unstructured forest environments from vision alone, outperforming pose-estimation baselines in simulation.
Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model
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abstract
We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networks to predict cost functions from input video which are directly suitable for online trajectory optimization with MPC. We demonstrate the method in a high speed autonomous driving scenario, where we use a single monocular camera and a deep convolutional neural network to predict a cost map of the track in front of the vehicle. Results are demonstrated on a 1:5 scale autonomous vehicle given the task of high speed, aggressive driving.
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2019 1verdicts
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Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
End-to-end multi-task regression learns flight commands for UAVs to explore unstructured forest environments from vision alone, outperforming pose-estimation baselines in simulation.