Deep Learning-based Obstacle Depth Estimation

• Main Authors: Lin, Wei-Yu, 林為瑀
• Other Authors: Chuang, Jen-Hui
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/gfa5w4

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === Obstacle detection and avoidance are crucial issues in robotics and unmanned vehicles. In these kind of applications, we usually use a front-view camera as the system’s visual inputs. Due to perspective projection, we cannot know the object depth using the front-view camera image. Thus, most of the obstacle avoidance system rely on extra hardware, like RGB-D sensor, to get the object depth information. In order to deal with the loss of object depth information, we modify the existing deep learning-based object detection architecture – YOLOv3 and add an extra object depth prediction module. And then use a pre-processed KITTI dataset to train our proposed unified model for object detection and depth prediction to resolve the depth information loss problem. Besides, we use AirSim to generate simulated aerial images and use them to train and test our proposed unified model to verify our model can fit in different data domains. The experiment results show that our model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset. As for our AirSim dataset, we find out the extra depth prediction module can boost the object detection performance and achieve higher precision and recall rates. Moreover, our model also perform very well for the depth prediction.