ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via
  Graph Optimization
Zongyang Chen Xianfei Pan Changhao Chen
Accurately and reliably positioning pedestrians in satellite-denied
conditions remains a significant challenge. Pedestrian dead reckoning (PDR) is
commonly employed to estimate pedestrian location using low-cost inertial
sensor. However, PDR is susceptible to drift due to sensor noise, incorrect
step detection, and inaccurate stride length estimation. This work proposes
ReLoc-PDR, a fusion framework combining PDR and visual relocalization using
graph optimization. ReLoc-PDR leverages time-correlated visual observations and
learned descriptors to achieve robust positioning in visually-degraded
environments. A graph optimization-based fusion mechanism with the Tukey kernel
effectively corrects cumulative errors and mitigates the impact of abnormal
visual observations. Real-world experiments demonstrate that our ReLoc-PDR
surpasses representative methods in accuracy and robustness, achieving accurte
and robust pedestrian positioning results using only a smartphone in
challenging environments such as less-textured corridors and dark nighttime
scenarios.