Tootfinder

ReNiL: Relative Neural Inertial Locator with Any-Scale Bayesian Inference
Kaixuan Wu (School of Computer Science, Wuhan University, Wuhan, China, School of Cyber Science and Engineering, Wuhan University, Wuhan, China), Yuanzhuo Xu (School of Computer Science, Wuhan University, Wuhan, China), Zejun Zhang (University of Southern California, Los Angeles, United States), Weiping Zhu (School of Computer Science, Wuhan University, Wuhan, China), Steve Drew (Department of Electrical and Soft…

ReNiL: Relative Neural Inertial Locator with Any-Scale Bayesian Inference
Pedestrian inertial localization is key for mobile and IoT services because it provides infrastructure-free positioning. Yet most learning-based methods depend on fixed sliding-window integration, struggle to adapt to diverse motion scales and cadences, and yield inconsistent uncertainty, limiting real-world use. We present ReNiL, a Bayesian deep-learning framework for accurate, efficient, and uncertainty-aware pedestrian localization. ReNiL introduces Inertial Positioning Demand Points (IPDPs)…