Bumping: A Bump-Aided Inertial Navigation Method for Indoor Vehicles Using Smartphones

Abstract

Equipped with accelerometers and gyroscopes, modern smartphones provide an appealing approach to infrastructure-free navigation for vehicles in indoor environments (for example parking garages). However, a smartphone-based inertial navigation system (INS) faces two serious problems. First, it is subject to errors that accumulate over time rather quickly, which may grow to a level that renders the navigation meaningless. Second, without human input or external references, the smartphone can hardly infer its initial position/velocity, which is the basis for distance calculation, since all that a smartphone can learn is its acceleration. This raises a practical concern, as users often need to start indoor navigation precisely when they are uncertain of their current whereabouts. In this paper, we present Bumping , a Bump-Aided Inertial Navigation method that significantly alleviates the above two problems. At the core of this method is a Bump Matching algorithm, which exploits the position information of the readily available speed bumps to provide useful references for the INS. The proposed method is easy to implement, requires no infrastructures, and incurs nearly zero extra energy. We conducted real experiments in tree parking garages of different environmental characteristics. The Bumping method produces an average position error of 4-5 m in these scenarios, improving the accuracy by up to 87.1 percent, compared to the basic inertial navigation method.