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Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15 Hz update rate on a dual-core 2 GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a simultaneous localization and mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.
Oskiper et al. (Fri,) studied this question.
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