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- W1959238714 abstract "We present a case-study in using specialized, physics-based software for high-fidelity environment and electro-opticalsensor modeling in order to produce simulated sensor data that can be used to train a multi-spectral perception system forunmanned ground vehicle navigation. This case-study used the Virtual Autonomous Navigation Environment (VANE)to simulate filtered, multi-spectral imaging sensors. The VANE utilizes ray-tracing and hyperspectral material propertiesto capture the sensor-environment interaction. In this study we focus on a digital scene of the ERDC test track inVicksburg, MS that has extremely detailed representation of the vegetation and ground texture. The scene model is usedto generate imagery that simulates the output of specialized terrain perception hardware developed by SouthwestResearch Institute, which consists of stereo pair of 3-channel cameras. The perception system utilizes stereo processing,the multi-spectral responses, and image texture features in order to create a 3-dimensional world model suitable foroffroad vehicle navigation, providing depth information and an estimated terrain class label for every pixel by utilizingmachine learning. While the process of training the perception system generally involves hand-labeling data collectedthrough manned missions, the ability to generate data for certain environments and lighting conditions represents anenabling technology for deployment in new theaters. We demonstrate an initial capability to simulate data and train theperception system and present the results compared to the system trained with real-world data from the same location." @default.
- W1959238714 created "2016-06-24" @default.
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- W1959238714 date "2015-10-13" @default.
- W1959238714 modified "2023-09-26" @default.
- W1959238714 title "Simulation of a multispectral, multicamera, off-road autonomous vehicle perception system with Virtual Autonomous Navigation Environment (VANE)" @default.
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- W1959238714 doi "https://doi.org/10.1117/12.2194372" @default.
- W1959238714 hasPublicationYear "2015" @default.
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