Real-World Testing of LiDAR-Inertial Based Navigation and Mapping for Precision Landing

The fusion of LiDAR and inertial measurements duringspacecraft descent and landing can be used to estimate alander’s navigation state and map the terrain below. Together,these data products can be used to enable safe and preciselanding on celestial bodies for which a priori orbital reconnaissanceis insufficient for hazard detection and avoidance.Unlike camera images used in visual terrain relative navigation,LiDAR scans are insensitive to changes in illumination; as aresult, the technique can be used to land in poorly lit areas,or at times of day when the lighting conditions are incongruentwith existing orbital imagery. In this paper, we extend previouswork in which we introduced a factor graph based smoothingapproach for LiDAR-inertial navigation and mapping. Whereasthe algorithms were previously tested on simulated data, thispaper presents testing on real-world data. Data from theAutonomous Landing Hazard Avoidance Technology (ALHAT)airplane flight tests in the Yucca Flats and Death Valley in2009 (FT3), the Morpheus vertical take off and landing flighttests at Kennedy Space Center in 2014 (FT6), and the landingof Perseverance and Ingenuity on Mars in 2021 (M2020) wereused to evaluate algorithm performance. In this paper, weextend our LiDAR-inertial technique to work with a variety ofranging technologies: single point laser altimetry (FT3), denseflash LiDAR (FT6), and six-beam radar (M2020). A thoroughperformance analysis for all three datasets is presented. Datasetpreparation, improvements in algorithm robustness, and outlierrejection, which were necessitated by the transition to realworlddata, are discussed.