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This work is motivated by a development of a portable and low-cost solution for road mapping in downtown area using a number of laser scanners and video cameras that are mounted on an intelligent vehicle. Sensors on the vehicle platform are considered to be removable, so that extrinsic calibrations are required after each sensors' setting up. Extrinsic calibration might always happen at or near measuremental sites, so that the facilities such as specially marked large environment could not be supposed as a given in the process. In this research, we present a practical method for extrinsic calibration of multiple laser scanners and video cameras that are mounted on a vehicle platform. Referring to a fiducial coordinate system on vehicle platform, a constraint between the data of a laser scanner and of a video camera is established. It is solved in an iterative way to find a best solution from the laser scanner and from the video camera to the fiducial coordinate system. On the other hand, all laser scanners and video cameras are calibrated for each laser scanner and video camera pair that has common in feature points in a sequential way. An experiment is conducted using the data measured on a normal street road. Calibration results are demonstrated by fusing the sensor data into a global coordinate system.