This article focuses on human navigation, by proposing a system for mapping and self-localization based on wearable sensors, i.e., a laser scanner and a 6 Degree-of-Freedom Inertial Measurement Unit (6DOF IMU) fixed on a helmet worn by the user. The sensor data are fed to a Simultaneous Localization And Mapping (SLAM) algorithm based on particle filtering, an approach commonly used for mapping and self-localization in mobile robotics. Given the specific scenario considered, some operational hypotheses are introduced in order to reduce the effect of a well-known problem in IMU-based localization, i.e., position drift. Experimental results show that the proposed solution leads to improvements in the quality of the generated map with respect to existing approaches. (C) 2011 Elsevier B.V. All rights reserved.