# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 2 Previous Work
In [25], the relative change in position between two image frames is first estimated based on time-differenced GNSS carrier phase measurements. The metric-accurate GNSS-derived change in position is exploited to initialize the otherwise unobservable scale in a monocular visual SLAM system. However, GNSS measurements are not incorporated any further once the absolute scale has been initialized. Unlike the current paper, the visual SLAM map is rendered in the arbitrary SLAM coordinate frame since only the relative change in position, and not the absolute position, was estimated based on GNSS measurements. The vision-GNSS fusion in the current paper is closely aligned with the bi-objective bundle adjustment (BA) optimization techniques previously reported in [17; 18; 20; 22; 23; 24]. In [17; 18; 20; 22], the traditional visual SLAM reprojection cost function is jointly minimized along with a GNSS position error term. The methods proposed in [23; 24] are also similar, but guarantee that the visual reprojection cost after incorporation of the GNSS term is not significantly greater than the visual-only case. However, none of these works showed significant empirical evidence of their efficacy on real-world vehicle data sets. Furthermore, collaborative mapping or multi-session improvement of the map was not discussed. Collaborative multi-agent mapping, without GNSS aiding, has also been extensively discussed in the literature [29; 30; 31; 32; 33; 34; 35]. Some of these proposed solutions require significant overlap in the field-of-view of the agents, or require that the relative pose transformation between the agents be known a priori [30; 32]. Other solutions, such as in [31; 33; 34; 35], enable collaboration by performing data association between non-concurrent mapping sessions where the relative pose transformation between the agents is unknown. The multi-session strategy employed in this paper is similar, but with an important distinction: none of the previous works on collaborative mapping have incorporated GNSS measurements in the map-building process. Without global referencing, the problem of data association between non-concurrent sessions becomes intractable. With no estimate of the pose for the mapping platform in relation to the existing map, data association must be attempted against the entire map. It is easily observed that such data association will become infeasible when scaled to city- or country-wide maps. The current paper proposes rendering and storage of digital maps in a global coordinate frame, such that a new mapping session can readily estimate its approximate pose in relation to the prior map, and perform data association on a small segment of the prior map that is expected to be in view of the vision system. The work presented in [36] is perhaps the most closely related to the current paper. In [36], a particular stretch of roadway is mapped 25 times with a low-cost sensor setup. However, [36] assumes, without detail, the availability of a lane-level accurate low-cost positioning module that provides the full 6-DoF pose for the mapping platform. This greatly simplifies the ensuing data association and mapping pipeline. No mention is made of the general setting of the roadway being mapped (open-sky highway, urban canyon, etc. ), and while the accuracy of the mapped traffic signs is adequately reported, localization within the map is not discussed, presumably since lane-level accurate positioning is already available. Meanwhile, the current paper only assumes the availability of a meter-level accurate code-phase-based GNSS receiver that provides 3D position estimates. Global localization accuracy of a vehicle operating within the multi-session map is presented as the key performance indicator.