Autonomous mapping systems execute multiple tasks that include navigation, location, and map generation via the collaborative work of multiple sensors. They are the object of a substantial research focus in the fields of robotics and remote sensing. Although the state-of-the-art mobile mapping systems typically found in ready-made vehicles or robots are reliable, they are rather large and heavy, their cost is high, and they generally use GPS and an inertial measurement unit to position, so their working environments are limited. After reviewing the current state of autonomous mapping systems, we describe the design and development of a small and lightweight autonomous mapping system (ASQ-6DMapSys) without GPS, which incorporates low-cost sensors and components. We describe the layout and selection strategy for sensors and other components in detail, and we present the design methodology for each subsystem. The ASQ-6DMapSys employs a two-dimensional (2D) lidar, an inclinometer, and two wheel encoders, which constitute a pose subsystem that uses extended Kalman filtering and simultaneous localization and mapping techniques to compute the pose of the vehicle body. A low-cost 3D lidar that we developed is also installed on the vehicle body, and the resultant data are aligned with the corresponding pose data of the vehicle body to build a 3D point cloud that describes the global geometry of the environment. We designed and developed every subsystem of the ASQ-6DMapSys, including the robot vehicle, so it will be easy to expand its functions in the future. The ASQ-6DMapSys performs well in indoor, outdoor, and tunnel environments, and the experiments in different environments show that the ASQ-6DMapSys is an effective, small, and lightweight autonomous mapping system with a high performance/price ratio.