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- W3126437874 abstract "Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications." @default.
- W3126437874 created "2021-02-15" @default.
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- W3126437874 date "2021-02-05" @default.
- W3126437874 modified "2023-10-16" @default.
- W3126437874 title "Building a Real-Time 2D Lidar Using Deep Learning" @default.
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- W3126437874 doi "https://doi.org/10.1155/2021/6652828" @default.
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