  We present DynamicSLAM: an indoor localization technique that eliminates the
need for the daunting calibration step. DynamicSLAM is a novel Simultaneous
Localization And Mapping (SLAM) framework that iteratively acquires the feature
map of the environment while simultaneously localizing users relative to this
map. Specifically, we employ the phone inertial sensors to keep track of the
user's path. To compensate for the error accumulation due to the low-cost
inertial sensors, DynamicSLAM leverages unique points in the environment
(anchors) as observations to reduce the estimated location error. DynamicSLAM
introduces the novel concept of mobile human anchors that are based on the
encounters with other users in the environment, significantly increasing the
number and ubiquity of anchors and boosting localization accuracy. We present
different encounter models and show how they are incorporated in a unified
probabilistic framework to reduce the ambiguity in the user location.
Furthermore, we present a theoretical proof for system convergence and the
human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM
using different Android phones shows that it can provide a localization
accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art
techniques by 55%, highlighting DynamicSLAM promise for ubiquitous indoor
localization.
