Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
IEEE Computer Graphics and Applications
Ultrasound-aided pedestrian dead reckoning for indoor navigation
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
HeadSLAM - simultaneous localization and mapping with head-mounted inertial and laser range sensors
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors
Proceedings of the 11th international conference on Ubiquitous computing
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Location and Navigation Support for Emergency Responders: A Survey
IEEE Pervasive Computing
Robotics and Autonomous Systems
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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.