State Observability through Prior Knowledge: Tracking Track Cyclers with Inertial Sensors

Inertial Navigation Systems suffer from unbounded errors on the position and orientation estimate. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. Analysing the state observability induced by prior knowledge motivates us to track bikers in track cycling races. In this paper, we show that the pose of the bikers can be estimated with an IMU as the only sensor by using a heightmap of the track and the knowledge that the biker drives forward. We present a dataset with three 60-round trials and evaluate the state estimate. We show that the influences of the priors match the expectation derived from state observability analysis.