Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

Abstract

High-definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer-grade sensors in mass-produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high-definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing-based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high-precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud-based map.