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Other AbstractEnvironmental perception is essential for the robot with artificial intelligence, and it should be firstly resolved. As the important parts of environmental perception, three-dimensional information acquisition as well as target detection and tracking has a wide potential applications in military, security and elder and disabled people assistance. This thesis focuses on the research on target detection and tracking for mobile robot. The main contents are as follows:
Firstly, the research background and significance of mobile robot and environmental perception are introduced. The research development of stereo vision and lidar is presented. The target detection and target tracking are then reviewed. The contents and structure of the thesis are also introduced.
Secondly, the research of 3D information acquisition is conducted. For binocular camera, the calibration of internal and external parameters and stereo matching are completed, and the depth map is acuqired. For monocular camera and multi-channel lidar, on the basis of the joint calibration, lidar point cloud can be projected into image coordinate system, which provides 3D information related to the target.
Thirdly, the method of target detection based on deep learning is studied and improved. To improve the real-time performance, cropping is applied to the SSD detection model, which can meet the real-time requirement of the embedded GPU platform. In the aspect of accuracy improvement, the result of target detection based on deep learning can be further precisely located with the combination of 3D information, which is used to achieve the initialization for subsequent target tracking.
Fourthly, the target tracking approaches based on the KCF tracker are presented. For monocular target tracking, a cascade locator is designed to realize the combination of the cropped SSD detection model and KCF tracker, which can alleviate the model drift and target partial occlusion problems. As for binocular target tracking, based on the depth distribution of the target extracted from the 3D information of the scene, a multi-feature KCF tracker with HOG, CN, LDP is designed to deal with the problems of occlusion and model drift.
Fifthly, a robotic experimental system for target detection and tracking is built. The mobile robot platform with its software framework is described. The mobile robot with binocular camera adopts the multi-feature KCF tracking approach based on the 3D information of the scene. When the robot senses the environment using monocular camera and multi-channel lidar, it adopts the KCF approach with HOG and CN for target tracking, where the target-related 3D information is provided by the lidar. The indoor and outdoor experiments of pedestrian following conducted on the mobile robot platform verify the effectiveness of the afore-mentioned approaches.
Finally, the conclusions are given and future work is addressed.
Keyword双目视觉 激光雷达 深度学习 Kcf 目标检测与跟踪 移动机器人
Document Type学位论文
Recommended Citation
GB/T 7714
张磊杰. 面向移动机器人的目标检测与跟踪方法研究[D]. 北京. 中国科学院研究生院,2017.
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zlj_移动机器人目标检测与跟踪研究_5(5312KB)学位论文 限制开放CC BY-NC-SA
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