Simultaneous localization and mapping (SLAM) is a widely researched topic in the field of robotics, augmented/virtual reality and more dominantly in self-driving cars. SLAM is a technique of building a map of the environment and estimating the state of the robot in the map in which it is moving, simultaneously. SLAM has been there for more than 30 years and has contributed significantly in the industry targeting from small scale driven applications to large scale, which resulted in the advent of this decade's self driving cars. This paper attempts to give an understanding and progress of SLAM in autonomous driving industry as well as briefly describes the SLAM techniques that have contributed significantly to the industry, which were especially evaluated on KITTI dataset. We have also attempted to compare various techniques that were presented and made a rough estimate on why the state of the art approach can be revised and refurnished to suit the complex understanding of the environment for effective localization. In the end we have briefly described the security threats related to autonomous driving industry and why this is alarming.