Design and development of an autonomous mobile robot for parking lot mapping

Abstract

Autonomous mobile robots have become a viable solution for advancing parking lot infrastructures to be autonomously patrolled and surveyed remotely. Towards this goal, this thesis presents a monocular vision-based perception system that generates a visually interpretable parking lot map using a constructed 1/3rd scale Security Patrol Robot (SPR). The proposed perception pipeline uses semantic segmentation, inverse perspective mapping, probabilistic line detection, and pose-based line clustering to approximate metric-scaled line marking representations. An incremental mapping algorithm uses robot localization to merge spatially related line detections and create a globally consistent map. Parking spots are detected based on mapped geometry and occupancy is classified using 3D vehicle detection. The end-to-end system is verified using data captured by the SPR in an outdoor parking lot environment. The results show the developed framework is capable of efficient parking lot mapping and spot detection under real-world conditions.