Identifying fundamental characteristics of a structure provide key information for structural health monitoring (SHM). To date, numerous researchers have reported tools and algorithms that can perform system identification, with a large portion of their application being contact-based sensors. Although dynamic responses of structures can be directly measured from contact-based sensors, the lifespan of those sensors being much shorter than that of the structure, requiring labor to deploy and maintain the sensors, etc., has led to the use of non-contact-based sensors. Among various non-contact-based sensors, some researchers have investigated the use of light detection and ranging (Lidar) sensors, which remotely acquire three-dimensional ranging information, mostly for static displacement measurement during construction. Thus, this paper presents an approach for system identification of structures using dynamic displacement measured from a multi-channel Lidar sensor. Hardware and mechanical attributes that limit the direct use of raw data from the Lidar are explored. Then, strategies to adjust the tilt axes and reduce the range uncertainties and data synchronization are proposed. Subsequently, two types of laboratory-scale structures are prepared for validation: a flexible cantilever beam and a four-story shear building. In both of the structures, the Lidar showed less than 5% error in estimating the first natural frequencies. Also, the mode shape has been estimated with high precision. The results demonstrate the ability of the Lidar for identifying dynamic characteristics of a structure. The potable feature of the Lidar will further allow full-scale monitoring of a large-scale civil infrastructure for SHM.