Real-time Monocular Depth Estimation with Extremely Light-weight Neural Network

Autor: Chiu, Mian-Jhong, 邱勉中
Rok vydání: 2019
Druh dokumentu: 學位論文 ; thesis
Popis: 107
Obstacle avoidance and environment sensing are crucial applications in autonomous driving and robotics. Among all types of sensors, camera is widely used in these applications because it can offer rich visual contents with relatively low-cost. Thus, using images from a single camera to perform depth estimation became one of the main focus in resent research works. However, prior works usually rely on highly complicated computation and power-consuming equipment to achieve such task; therefore, we focus on developing a real-time light-weight system for depth prediction in this thesis. Based on the well-known encoder-decoder architecture, we propose a supervised learning-based CNN with detachable decoders that outputs predicted depth maps with multiple resolutions. We also formulate a novel multi-task loss function for each decoder block, which considers both depth map and semantic segmentation simultaneously to encourage model convergence as well as to speed up the training process. To train our model on KITTI dataset, we generate depth map and semantic segmentation via PSMNet and DeepLabV3, respectively as ground truth, and test various pre-processing methods. We also collect a synthetic dataset in AirSim with a wide range of cameras views to evaluate the proposed depth estimation approach in terms of robustness. Via a series of ablation studies and experiments, it is validated that our model can efficiently performs real-time depth prediction with few parameters and fairly low computation cost, with the best trained model outperforms previous works on KITTI dataset for various evaluation matrices. Trained and tested on our AirSim dataset, our model also shown to be able to deal with images captured with quite different camera poses and altitudes.
Databáze: Networked Digital Library of Theses & Dissertations
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