慣性センサを用いた人の移動様式の変化に頑強な位置推定

抄録

Indoor tracking is in demand in various field. For example, pedestrian navigation, activity/health monitoring, augmented reality can be sited. VIO(Visual-Inertial Odometry) method has been established as a localization method, but it is difficult to estimate in the low visibility location. Although this problem can be solved by localization by the inertial sensor alone, it is known that large errors will occur. Recent studies have shown that the error can be significantly alleviated by using deep neural networks. Human beings move variously not only to walking or running. However, there are few studies focusing on this, and estimation accuracy for movement including multiple kind of movements such as including both walking and running is low. In this research we aim to develop a robust localization method for changes in human movement using inertial sensor.