Velocimeter LIDAR-Based Bulk Velocity Estimation for Terrain Relative Navigation Applications

摘要

View Video Presentation: https://doi.org/10.2514/6.2022-2208.vid A novel batch state estimation approach to estimate the translational and angular velocity states of a vehicle using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for use in Terrain and Hazard Relative Navigation (TRN/HRN) applications is presented in this paper. The velocimeter LIDAR is capable of measuring three dimensional position and line-of-sight (LOS) velocity associated with every pixel in the field-of-view (FOV). This new batch state estimation methodology is shown to provide accurate and statistically consistent velocity estimates with no a priori information. In contrast to traditional computer vision ap- proaches for TRN, the proposed technique is not dependent on image features. This alleviates the need for accurate feature detection and correspondence to a predefined map making it suitable for unknown operating environments. Following a detailed development of the mathe- matical details associated with the batch state estimation methodology, the efficacy and utility of the proposed algorithms are evaluated through emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory.