Date of Award
Doctor of Philosophy (PhD)
This dissertation proposes a novel scalable framework that unifies unsupervised object discovery, detection, tracking and reconstruction (DDTR) by using dense visual simultaneous localization and mapping (SLAM) approaches. Related applications for both indoor and outdoor environments are presented.
The dissertation starts by presenting the indoor scenario (Chapter 3), where DDTR simultaneously localizes a moving time-of-flight camera and discovers a set of shape and appearance models for multiple objects, including the scene background. The proposed framework represents object models with both a 2D and 3D level-set, which used to improve detection, 2D-tracking, 3D-registration and importantly subsequent updates to the level-set itself. An example of the proposed framework in simultaneous appearance-based DDTR using the time-of-flight camera and a robot manipulator is also presented (Chapter 4).
After presenting the indoor experiments, we extend DDTR to the outdoor environments. Chapter 5 presents a dense visual-inertial SLAM framework, in which inertial measurements are combined with dense stereovision for pose tracking. A rolling grid scheme is used for large-scale mapping. Chapter 6 proposes a scalable dense mapping pipeline that uses range data from various range sensors (e.g. the time-of-flight camera, stereo camera and multiple lasers) to generate a very high resolution, dense citywide map in real-time (700Hz on average).
Finally, Chapter 7 presents the application of DDTR in autonomous driving, including citywide dense SLAM, truncated signed distance function based vehicle six degree of freedom localization and object discovery, and the simultaneous tracking and reconstruction of vehicles. The results demonstrate a scalable and unsupervised framework for object discovering, detection, tracking and reconstruction that can be used for both indoor and outdoor applications.
Ma, Lu, "Scalable Unsupervised Dense Objects Discovery, Detection, Tracking and Reconstruction" (2016). Computer Science Graduate Theses & Dissertations. 128.