Simultaneous localization and mapping for autonomous bicycles

Bicycles and similar two-wheeled vehicles are a scientifically interesting category of means of transportation, whose simultaneous localization and mapping problem, in contrast to other ground vehicles, has yet to be examined in depth and fully resolved. In this article, we introduce for the first time a comprehensive theoretical framework for the application of simultaneous localization and mapping specifically for autonomous bicycles and akin vehicles, based on the kinematics and dynamics models of the bicycle and the specific effects they have on the motion/odometry and measurement models that are essential for the solution of the simultaneous localization and mapping problem. In addition, we present our laboratory's first autonomous bicycle platform and its functional sensor system and sensor rotation pattern, specifically adjusted to the special characteristics of bicycles. Moreover, we investigate the effect of the bicycle frame roll, the main uncertainty factor of the bicycle simultaneous localization and mapping problem, on the overall simultaneous localization and mapping performance. The experimental results performed on our bicycle platform verify the potency of our proposed modeling and simultaneous localization and mapping application framework and provide further insight on future improvements for the two-wheeled vehicle simultaneous localization and mapping problem.