Precise Localization and Mapping in Indoor Parking Structures via Parameterized SLAM

Abstract

This paper addresses a computationally efficient approach to localization and mapping in an indoor parking garage in the context of simultaneous localization and mapping. A parameterized map-building approach is introduced and implemented to represent the surrounding structures using a small number of geometric parameters. These parameters are obtained from horizontally and vertically ordered 3D LIDAR measurements and incorporated into an online filter to simultaneously estimate the map parameters and localize the vehicle. This approach enables the high-precision navigation and memory-efficient map representation of an environment with man-made structures with no need of global positioning system or external position fixes. Driving experiments were performed in indoor parking garages to verify and demonstrate the performance of the proposed localization and mapping approach.