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1. VELOCIMETER LIDAR-BASED RELATIVE RATE ESTIMATION FOR AUTONOMOUS RENDEZVOUS, PROXIMITY OPERATIONS, AND DOCKING APPLICATIONS

   Adams, Davis ( February 2022 , 44th annual AAS Guidance, Navigation and Control Conference)

   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.
2. Contact-aided invariant extended Kalman filtering for robot state estimation

   https://doi.org/10.1177/0278364919894385  

   Hartley, Ross ; Ghaffari, Maani ; Eustice, Ryan M ; Grizzle, Jessy W ( March 2020 , The International Journal of Robotics Research)

   Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). In this work, we develop a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design. This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points. We show that the error dynamics follows a log-linear autonomous differential equation with several important consequences: (a) the observable state variables can be rendered convergent with a domain of attraction that is independent of the system’s trajectory; (b) unlike the standard EKF, neither the linearized error dynamics nor the linearized observation model depend on the current state estimate, which (c) leads to improved convergence properties and (d) a local observability matrix that is consistent with the underlying nonlinear system. Furthermore, we demonstrate how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions. We compare the convergence of the proposed InEKF with the commonly used quaternion-based extended Kalmanmore »filter (EKF) through both simulations and experiments on a Cassie-series bipedal robot. Filter accuracy is analyzed using motion capture, while a LiDAR mapping experiment provides a practical use case. Overall, the developed contact-aided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in system.« less
3. Spectral correction of turbulent energy damping on wind LiDAR measurements due to range-gate averaging

   https://doi.org/10.5194/amt-2020-27  

   Puccioni, Matteo ; Iungo, Giacomo Valerio ( February 2021 , Atmospheric measurement techniques)

   Continuous advancements in LiDAR technology have enabled compelling wind turbulence measurements within the atmospheric boundary layer with range gates shorter than 20 m and sampling frequency of the order of 10 Hz. However, estimates of the radial velocity from the back-scattered laser beam are inevitably affected by an averaging process within each range gate, generally modeled as a convolution between the actual velocity projected along the LiDAR line-of-sight and a weighting function representing the energy distribution of the laser pulse along the range gate. As a result, the spectral energy of the turbulent velocity fluctuations is damped within the inertial sub-range with respective reduction of the velocity variance, and, thus, not allowing to take advantage of the achieved spatio-temporal resolution of the LiDAR technology. In this article, we propose to correct this turbulent energy damping on the LiDAR measurements by reversing the effect of a low-pass filter, which can be estimated directly from the LiDAR measurements. LiDAR data acquired from three different field campaigns are analyzed to describe the proposed technique, investigate the variability of the filter parameters and, for one dataset, assess the procedure for spectral LiDAR correction against sonic anemometer data. It is found that the order ofmore »the low-pass filter used for modeling the energy damping on the LiDAR velocity measurements has negligible effects on the correction of the second-order statistics of the wind velocity. In contrast, its cutoff frequency plays a significant role in the spectral correction encompassing the smoothing effects connected with the LiDAR gate length.« less
4. Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications

   https://doi.org/10.1007/s10846-022-01676-3  

   Ostertag, Michael ; Atanasov, Nikolay ; Rosing, Tajana ( August 2022 , Journal of Intelligent & Robotic Systems)

   This paper considers persistent monitoring of environmental phenomena using unmanned aerial vehicles (UAVs). The objective is to generate periodic dynamically feasible UAV trajectories that minimize the estimation uncertainty at a set of points of interest in the environment. We develop an optimization algorithm that iterates between determining the observation periods for a set of ordered points of interest and optimizing a continuous UAV trajectory to meet the required observation periods and UAV dynamics constraints. The interest-point visitation order is determined using a Traveling Salesman Problem (TSP), followed by a greedy optimization algorithm to determine the number of observations that minimizes the maximum steady-state eigenvalue of a Kalman filter estimator. Given the interest-point observation periods and visitation order, a minimum-jerk trajectory is generated from a bi-level optimization, formulated as a convex quadratically constrained quadratic program. The resulting B-spline trajectory is guaranteed to be feasible, meeting the observation duration, maximum velocity and acceleration, region enter and exit constraints. The feasible trajectories outperform existing methods by achieving comparable observability at up to 47% higher travel speeds, resulting in lower maximum estimation uncertainty.
5. LiDAR-OSM-Based Vehicle Localization in GPS-Denied Environments by Using Constrained Particle Filter

   https://doi.org/10.3390/s22145206  

   Elhousni, Mahdi ; Zhang, Ziming ; Huang, Xinming ( July 2022 , Sensors)

   Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter’s final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps.