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This paper presents a navigation system for autonomous rendezvous, proximity operations, and docking (RPOD) with respect to non-cooperative space objects using a novel velocimeter light detection and ranging (LIDAR) sensor. Given only raw position and Doppler velocity measurements, the proposed methodology is capable of estimating the six degree-of-freedom (DOF) relative velocity without any a priori information regarding the body of interest. Further, the raw Doppler velocity measurement field directly exposes the body of interest’s center of rotation (i.e. center of mass) enabling precise 6-DOF pose estimation if the rate estimates are fused within a Kalman filter architecture. These innovative techniques are computationally inexpensive and do not require information from peripheral sensors (i.e. gyroscope, magnetometer, accelerometer etc.). The efficacy of the proposed algorithms were evaluated via emulation robotics experiments at the Land, Air and Space Robotics (LASR) laboratory at Texas A&M University. Although testing was completed with a single body of interest, this approach can be used to online estimate the 6-DOF relative velocity of any amount of non-cooperative bodies within the field-of-view. 

This paper presents a Multiplicative Extended Kalman Filter (MEKF) framework using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for Terrain Relative Navigation (TRN) applications. The newly developed velocimeter LIDAR is capable of providing simultaneous position, Doppler velocity, and reflectivity measurements for every point in the point cloud. This information, along with pseudo-measurements from point cloud registration techniques, a novel bulk velocity batch state estimation process and inertial measurement data, is fused within a traditional Kalman filter architecture. Results from extensive emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory and Monte Carlo simulations are presented to evaluate the efficacy of the proposed algorithms.