# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 1 Introduction  
Localization is one of the primary operations that connected and automated vehicles must perform, both to navigate from one location to another and to interact with each other and with their surroundings within a mapped environment. Satellite-based navigation sensors have historically been the unrivalled sensor of choice for navigating from source to destination. However, the high-reliability sub-50-cm precision demanded by automated vehicles for lane-keeping and other applications, especially in urban areas, has significantly changed this landscape [1]. In most automated vehicles being developed, the Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) is relegated to a secondary sensor whose role is to loosely constrain (within a few meters) the primary localization sensors, usually camera(s) and/or LiDAR, to a global reference frame when building a digital map. The vehicles then locate themselves to decimeter accuracy within this digital map. Automated driving does not necessarily demand sub-50-cm agreement between the coordinates of a given point in the digital map and the coordinates of the same point in a well-defined globalreference frame. Rather, local self-consistency and accurate localization within the digital map is of greater importance. However, consistency of the digital map with a global coordinate frame is likely to become a pre-requisite for cooperative automated driving. Automated driving can be made more efficient (e.g., by platooning) and safe if basic safety information such as vehicle location, velocity, intent, etc. are shared among neighboring agents through vehicle-to-vehicle (V2V) and/or vehicle-to-everything (V2X) communication [2; 3]. In many cases, such information can be perceived independently by each agent via its exteroceptive sensors. But in other situations--for example at a blind corner or during a left turn maneuver--full situational awareness will require external sensors--on other vehicles or on infrastructure--that wirelessly communicate data to the ego-vehicle. If all collaborating vehicles navigate within the same digital map, exchange of information can be performed with sub-50-cm precision [4; 5], even if the map itself is only globally accurate to a few meters. However, it is unlikely that automated vehicles from different manufacturers will rely on a common digital map. Consequently, the precision of the exchanged vehicle position is lower-bounded by the disagreement on the coordinates of the same physical location between different maps. Thus, exchange of accurate vehicle pose among vehicles, as well as other associated high-level information such as sensor data in the vehicle's body frame, will demand consistency among, or translation between, different digital maps. Standard code-phase-based GNSS position measurements, such as those provided by all mass-market GNSS receivers, may be biased by as much as 3-5 meters on any given mapping session. Maps anchored by these measurements may not exhibit lane-level consistency with each other. One possible solution is to create digital maps with decimeter-accurate carrier-phase differential GNSS (CDGNSS) systems [6]. However, at current prices, such systems can only be installed on a limited fleet of specialized mapping vehicles. Precise point positioning (PPP) techniques offer a low-cost alternative to CDGNSS, but the frequent cycle-slipping experienced in urban areas impedes the convergence of PPP techniques [7]. While mapping with a specialized fleet is feasible for urban areas, it is time-consuming and cumbersome to create and maintain maps of entire continents. Thus, a key enabler for large-scale up-to-date maps will be enlisting the help of the very consumer vehicles that need the map to build and update it. Consumer vehicles will likely be equipped with low-cost consumer-grade sensor suites.