Graph Optimization Methods for Large-Scale Crowdsourced Mapping

Automotive players have recently shown an increasing interest in high-precision mapping, with the aim of enhancing vehicles safety and autonomy. Nevertheless, the acquisition, processing, and updates of accurate maps remains an economic challenge. Collaborative mapping through vehicles crowdsourcing represents a promising solution to tackle this problem. However, the potential scalability and accuracy provided by such an approach have yet to be studied and assessed. In this paper, we study the use of graph optimization in the scope of collaborative mapping. We build a map of geo-localized landmarks by crowdsourcing observations from multiple vehicles, and applying several successive map updates. We present different strategies to adapt graph optimization to the crowdsourced approach, and compare their performances in terms of map quality and scalability on simulation data. We show the critical requirement, in a long-term context, to ensure consistency of the map updates, and we propose a scalable solution which is able to build an accurate map of geolocalized landmarks.