The automotive industry currently has been working on developing various levels of autonomy to assist in different Advanced Driver Assistance Systems (ADAS) with the ultimate aim of moving closer to the realization of an autonomous vehicle. For such ADAS, the industry has been using multiple sensors like Cameras, Radar, LiDAR, etc. LiDAR has been at the forefront of the research as it provides extremely rich and precise information about the surrounding. In research Motorized Optomechanical LiDAR has been used for almost a decade now. However, it can not be deployed in mass-produced vehicles because of how expensive the sensor is, thus, limiting it to academia without practical use. Therefore, the industry has been working on a different category of LiDAR namely Microelectromechanical Systems (MEMS) LiDAR. Which uses solid-state technology instead of servo motors. Thus, they are compact and much cheaper in cost, making them feasible to deploy in mass-produced vehicles.
There has been a considerable amount of research already done on Motorized Optomechanical LiDAR, therefore, one must take advantage of it to learn about MEMS LiDAR. The domain of Transfer Learning provides an opportunity to learn from the accumulated knowledge of Motorized Optomechanical LiDAR (Source Domain) and use it to reduce the learning time on MEMS LiDAR (Target Domain). Thus, reducing the effort, cost, and time to research MEMS LiDAR technology.
This thesis explores the Domain Mapping strategy of transfer learning for point clouds. A scan pattern-based domain mapping approach has been described in this thesis to reduce the domain gap between the source and target domain. Moreover, a point cloud densification pipeline that utilizes a depth completion network has been described to further reduce the gap between the two domains. Since semantic segmentation is one of the most actively researched tasks in the industry as it provides the researcher with in-depth information about the environment which could be used in activities like lane detection, parking assist, etc, therefore, this thesis utilizes it as the proxy for evaluating the performance of the domain mapping strategy. It compares a few domain mapping strategies like cropping the Field of View, an adaptation based on scanning pattern, and densification & adaptation to simulate the density of points in the source domain similar to the target domain. This thesis verifies that domain mapping strategy is a suitable solution to learn from Motorized Optomechanical LiDAR and use it to enhance performance on MEMS LiDAR and therefore reduce the data required for research of MEMS.