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.

翻译：在无卫星条件下对行人进行精确且可靠的定位仍是一项重大挑战。行人航位推算（PDR）通常利用低成本惯性传感器估计行人位置，但其易受传感器噪声、步数检测错误及步长估计不准导致的漂移影响。本文提出ReLoc-PDR——一种结合PDR与图优化视觉重定位的融合框架。ReLoc-PDR利用时间相关的视觉观测与学习描述符，在视觉退化环境中实现鲁棒定位。基于图优化并采用Tukey核的融合机制可有效校正累积误差并抑制异常视觉观测的影响。真实场景实验表明，在纹理缺失走廊、暗夜场景等挑战性环境中，仅需智能手机即可实现精确鲁棒的行人定位，且性能在准确性与鲁棒性上均超越代表性方法。