In this paper, we compare various lidar point cloud merge algorithms for 3D map generation and use of high definition maps for autonomous vehicles (AV) localization. An autonomous vehicle may have a variety of sensors, including cameras, lidars and GPS sensors. Each sensor technology has its own pros and cons, for example GPS may not be very effective in a city environment with high-rise building; Cameras may not be very effective in poorly illuminated environments; and Lidars simply generate a relatively dense local point cloud. In a typical autonomous vehicle system, all of these sensors are present and sensor fusion algorithms are used to extract the most accurate information. By using our AV research vehicle, we drove on our university campus and recorded RTK-GPS (ZED-F9P) and Velodyne Lidar (VLP-16) data in a time synchronized fashion. In this paper, we focus on two different but related problems. The first one is, comparison of different point cloud merge algorithms for building the 3D map of the campus (a.k.a. high-definition map). The second one is a localization problem, given a high-definition map of the environment and a local point cloud data generated by a single lidar scan, determine the AV research vehicle's location. We will present a detailed analysis by using experimental data, and compare various merge and localization algorithms.