Active environment perception and autonomous place recognition play a key role for mobile robots to operate within a cluttered indoor environment with dynamic changes. This paper presents a 3-D-laser-based scene measurement technique and a novel place recognition method to deal with the random disturbances caused by unexpected movements of people and other objects. The proposed approach can extract and match the Speeded-Up Robust Features (SURFs) from bearing-angle images generated by a self-built rotating 3-D laser scanner. It can cope with the irregular disturbance of moving objects and the problem of observing-location changes of the laser scanner. Both global metric information and local SURF features are extracted from 3-D laser point clouds and 2-D bearing-angle images, respectively. A large-scale indoor environment with over 1600 m2 and 30 offices is selected as a testing site, and a mobile robot, i.e., SmartROB2, is deployed for conducting experiments. Experimental results show that the proposed 3-D-laser-based scene measurement technique and place recognition approach are effective and provide robust performance of place recognition in a dynamic indoor environment.