Pose-graph based Crowdsourced Mapping Framework

Abstract

Autonomous vehicles are dependent on High Definition (HD) maps. The process of generating and updating these maps is slow, expensive, and not scalable for the whole world. Crowdsourcing vehicle sensor data to generate and update maps is a solution to the problem. In this paper, we propose and evaluate an end-to-end pose-graph optimization-based mapping framework using crowdsourced vehicle data. The in-vehicle data acquisition framework and the cloud-based mapping framework that fuses data from a consumer-grade Global Navigation Satellite System (GNSS) receiver, an odometry sensor, and a stereo camera is described in detail. We focus on using stereo image pairs for loop-closure detection to combine crowdsourced data from different sessions that are affected by GNSS biases. We evaluate our framework on a data-set of more than 180 km recorded around the Eindhoven area. After the map generation process, the results exhibit a 56.23% improvement in maximum offset error and a 24.39% improvement in precision around the loop-closure area.