Cycling dead reckoning for enhanced portable device navigation on multi-gear bicycles

Abstract

Based on the advancements in micro-electric mechanical systems (MEMS), lightweight and small-size inertial sensors are commonly used in various devices today. These advancements have dramatically increased the number of consumer devices that utilize MEMS-based sensors and the number of applications developed to promote interaction with those consumer devices. One application is navigation, which can be used to provide route guidance and other useful information to the user. MEMS inertial sensors can be used for navigation; however, they need aiding from other sources, such as updates from global navigation satellite system (GNSS) or other aiding sources. Nevertheless, using MEMS-based portable devices for cycling applications, in any orientation and without careful mounting or any external updates, is not enough for achieving acceptable navigation performance. We propose a new module to improve the use of low-cost motion sensors as a portable navigator for cycling without putting any constraints on the user and therefore working in different environments and in any device orientation. The proposed multi-gear cycling dead reckoning module involves an adaptive modeling algorithm. When trained, the models are used to enhance navigation. In cases where GNSS is not available or is degraded, this module can assist the integrated navigation solution. The proposed solution is general enough to apply to any type of bicycle and is also adaptive to various tire sizes and gears, as well as different cyclists. The experimental results demonstrate the performance and usefulness of the proposed solution.