Velocimeter light-detection-and-ranging (LIDAR)–informed batch and sequential estimation approaches for terrain relative navigation applications are developed in this paper. State-of-the-art velocimeter LIDAR sensors are capable of delivering simultaneous three-dimensional relative position, relative Doppler velocity, and reflectivity measurements for every point in the field of view. The proposed batch estimation algorithm yields accurate and statistically consistent vehicle velocity estimates in unknown (uncooperative) environments. This work presents novel pointwise relative position and Doppler velocity measurement models that can be utilized in the presence of known (cooperative) environments. Rate estimates obtained by the batch estimator are fused with the pointwise measurement models and classical inertial measurement unit formulations in the form of a multiplicative extended Kalman filter. This Doppler LIDAR-based sensor fusion approach yields informative terrain relative navigation solutions suitable for both cooperative and uncooperative environments. Results from extensive emulation robotics testing performed at NASA Johnson Space Center and Texas A&M’s Land, Air, and Space Robotics laboratory are presented to validate the proposed methodologies.

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