LiDAR-Based Map Relative Localization Performance Analysis for Landing on Europa

This paper presents preliminary simulations and analyses done to assess the feasibility of performing Map Relative Localization (MRL) with the Europa Lander LiDAR being developed for the Europa Lander Pre-Phase A concept. Map Relative Localization is the process of determining the horizontal position of a lander with respect to an onboard, a-priori map, by comparing the map to sensor observations of the terrain during deorbit, descent, and landing (DDL). Although kilometer-scale position knowledge is commonly available during DDL, landing in hazard-rich environments requires position errors of 100 m or less. Prior knowledge in the case of Europa Lander will be visual and topographic maps collected by the upcoming Europa Clipper mission. The Mars 2020 Lander Vision System (LVS) uses images from a camera to localize with respect to visual maps. This technology, as well as a 3D imaging LiDAR in development for hazard detection, is currently baselined for the Europa Lander Pre-Phase A concept. This paper investigates the potential use of the hazard detection LiDAR to perform MRL with respect to a 3D digital elevation model (DEM) provided by the Europa Clipper mission, as an alternative or backup solution to passive optical MRL. Compared to passive optical MRL, one advantage of LiDAR-based localization is that it is insensitive to lighting conditions, potentially relaxing requirements on synchronizing map acquisition and landing time of day. To analyze LiDAR based MRL performance, six representative terrains are synthetically up-sampled from Galileo-derived maps of Europa to a resolution of 0.5 m/px and covering an area of 4 km by 4 km. These maps are used as ground-truth to generate simulated noisy a-priori onboard topographic maps expected from Europa Clipper as well as simulated LiDAR DEMs generated at an altitude of 5 km during Europa Lander DDL. The simulated LiDAR DEM is matched against the simulated map via 2D normalized cross-correlation, exploiting the accurately known spacecraft attitude to avoid the need for more computationally intensive algorithms such as Iterative Closest Point (ICP). Two sources of measurement error are identified for analysis: 1) additive Gaussian noise in the range measurements from the Europa Lander LiDAR and the Europa Clipper derived maps and 2) errors in the LiDAR DEM induced by errors in the Europa Lander state estimate which is used to de-warp the LiDAR scan data into a DEM format. We assess the effect of each of these types of errors independently on matching performance as well as the overall performance when all types of error are introduced. Additionally, we present the result of a sensitivity study to terrain frequency content.