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Growing popularity of location-dependent mobile applications is continuously stimulating a demand for localization technology. However, in spite of significant research effort in the past decade, precise positioning in indoor environments is still an open problem. In this paper, we propose a novel type of indoor localization system that provides mobile phone users in a pedestrian crowd with their own position information of sub-meter accuracy by effectively utilizing a powerful pedestrian tracking capability of laser range scanners ( i.e., laser-based distance measurement sensors). Although the laser-based tracking system can precisely detect presence of pedestrians at each location in its sensing region, the location information is not associated with any mobile phone users and thus it basically cannot provide the users' own locations. To remove this limitation, we focus on spatio-temporal proximity patterns between mobile phones, which can be detected by peer-to-peer short-range wireless communication ( e.g., Bluetooth). By examining consistency between the communication logs and proximity between anonymous trajectories detected by laser-based tracking, our system identifies a trajectory that corresponds to each mobile phone user to offer their own position information. Through extensive simulations and field experiments, we show that our system can achieve trajectory identification accuracy of up to 91 %. [ABSTRACT FROM AUTHOR]

Most map building methods employed by mobile robots are based on the assumption that an estimate of robot poses can be obtained from odometry readings or from observing landmarks or other robots. In this paper we propose methods to build a global geometric map by integrating scans collected by laser range scanners without using any knowledge about the robots' poses. We consider scans that are collections of line segments. Our approach increases the flexibility in data collection, since robots do not need to see each other during mapping, and data can be collected by multiple robots or a single robot in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments. [ABSTRACT FROM AUTHOR]