Sangkyung Sung

Automotive radar has emerged as a pivotal technology for navigation in GNSS-denied environments, offering superior robustness to adverse weather and fluctuating lighting conditions compared to vision or LiDAR-based sensors. Despite these advantages, the inherent sparsity and noise of radar measurements often lead to degraded estimation accuracy and system reliability. To address these challenges, various radar-based localization frameworks have been explored, ranging from optimization-based and Extended Kalman Filter (EKF) approaches fused with Inertial Measurement Units (IMUs) to point cloud registration techniques like Iterative Closest Point (ICP). While filter-based methods are favored in multi-sensor fusion for their proven stability, ICP is widely utilized for high-precision pose estimation in point-cloud-centric systems. In this study, we propose a novel Radar-Inertial Odometry (RIO) framework that synergistically integrates ICP-based relative pose estimation with model-based sensor fusion. The proposed methodology leverages relative transformations derived from ICP alongside ego-velocity estimations obtained from radar Doppler measurements. To effectively incorporate relative ICP constraints, a stochastic cloning technique is implemented to augment previous states and their associated covariances, ensuring that the uncertainty of historical poses is explicitly accounted for. The performance of the proposed method is validated using public open-source datasets, demonstrating higher localization accuracy and more consistent performance compared to existing algorithms used for comparison.

This study proposes an advanced integrated navigation algorithm based on a monocular camera for cooperative proximity missions of spacecraft in a low Earth orbit (LEO) environment. The proposed framework involves a camera mounted on the chaser spacecraft and LED markers attached to the target spacecraft, where pixel coordinates extracted from camera images are directly utilized as measurements in a tightly coupled extended Kalman filter. Unlike conventional loosely coupled methods based on Perspective-n-Point (PnP) pose estimation, the proposed approach integrates pixel-level feature measurements directly into the filter, reducing computational cost and enabling robust state estimation with a significantly smaller number of valid feature points. To further enhance navigation accuracy, an extended observation matrix incorporating the relative geometric configuration of the LED markers is introduced. Additionally, a dual-pattern LED marker configuration is employed to overcome the limited field of view of the camera. The performance of the proposed algorithm is validated through both Gazebo simulations and lab-scale experiments, demonstrating that the extended observation model significantly contributes to reducing navigation errors.

This paper proposes a strategic approach to simultaneously achieve collision avoidance and non- line-ofsight (NLOS) clearance during the reconfiguration of multivehicle flight trajectories. In many trajectory reconfiguration cases, hazardous situations are defined as constraints, which are typically convexified through a convex relaxation process. For securing NLOS clearance, the paper exploits a sophisticated technique to convexify the constraint related to the line-of-sight disruption phenomenon by developing the conic relation within a relative state framework. These constraints are then applied to the model predictive control (MPC)-based guidance method to generate trajectories for each vehicle, incorporating an indirect linear relation designed to connect vehicle dynamics and the MPC-guided trajectories. The proposed convexification scheme is shown to offer advantages in implementing a one-shot algorithm compared to other iterative methods. The effectiveness of the proposed method is verified through simulations, demonstrating superior performance in collision and line-ofsight disruptive scenarios.

This paper proposes a pose estimation algorithm using INS and LiDAR for precise cooperative relative navigation between target and chaser spacecraft in a close docking mission scenario. Previous cooperative algorithms have proposed estimating position and pose transformations using typical matching methods or to pre-extract and utilize features from point cloud data. However, in the case of general proximity rendezvous docking, a straight-line approach scenario with very few changes in attitude is usually assumed, and, in this case, pose estimation using simple matching techniques or feature point extraction leads to inaccurate results. To solve this problem, this paper performed a principal component analysis (PCA) based on ICP to align the initial transformation matrix. To keep the distribution of point cloud data constant, the point cloud at the time of docking was applied to ICP to minimize the change in the distribution of point clouds over time. Finally, we designed an EKF filter that estimates the relative position, velocity, and attitude using the INS model and combines it with the relative pose estimated from the point cloud; the proposed method showed the results of estimating the relative pose more effectively than the previous method. The simulation and experiment showed more accurate estimation results than the ICP method in position and attitude, respectively. In particular, in the case of position, both the simulation and experiment showed 0.46 m and 0.32 m better estimation results in the z-axis. Also, attitude estimation showed 0.11° and 2.66° better results in roll and 0.01° and 0.34° better results in pitch. This shows that the proposed algorithm provided better pose estimation results in the docking scenario in a straight line.

This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant challenges such as the magnetic vector distortions and model uncertainties caused by motor noise, which degrade attitude estimation and limit the effectiveness of traditional Extended Kalman Filter (EKF)-based fusion methods. To mitigate these issues, a Tightly Coupled Unscented Kalman Filter (TC UKF) was developed to enhance robustness and navigation accuracy in dynamic environments. The proposed Unscented Kalman Filter (UKF) demonstrated a superior attitude estimation performance within a 6 m coil spacing area, outperforming both the MPS 3D LS (Least Squares) and EKF-based approaches. Furthermore, hyperparameters such as alpha, beta, and kappa were optimized using the Sequential Importance Resampling (SIR) process of the Particle Filter. This adaptive hyperparameter adjustment achieved improved navigation results compared to the default UKF settings, particularly in environments with high model uncertainty.

This study proposes a sensor integration design of magnetic positioning system (MPS) for achieving robust navigation performance. Despite consistent permeability of AC magnetic fields, signal strength that is inversely proportional to the square of the distance greatly constrains the operational range of the MPS. Additionally, the signal becomes vulnerable to environmental noise with deteriorated signal-to-noise ratio, which may bring about partial signal outage from some magnetic transmitter. In this background, this paper proposes the tightly coupled MPS/INS integrated navigation system to overcome these shortcomings. Instead of implementing isolated pose estimation via multiple magnetic measurements, it is designed each magnetic field vector is independently incorporated in the INS mechanization framework. As a result, successful filter correction has been guaranteed in the constrained magnetic measurement environments, thereby obtaining results that ensure pose estimation performance in an extended range with poor signal quality or even in a partial transmitter outage case. Besides, the proposed method contributes to demonstrate improved estimation performance in the navigation filter by providing more accurate magnetic predictions and measurement residuals compared to the traditional dipole technique. The proposed algorithm is demonstrated through trajectory experiments with an unmanned vehicle platform in outdoor domain with different ranges and partial signal occlusion scenario. When compared with the reference system, the robust navigation results using the MPS/INS TC algorithm exhibited pose estimation error of decimeters in position and 2 degree in attitude along with computational efficiency. Moreover, robust estimation is observed even in a partial signal occlusion in contrast with previous diverging results from MPS-only or MPS/INS loosely coupled system.

This study proposes an enhanced integration algorithm that combines the magnetic field-based positioning system (MPS—Magnetic Pose Estimation System) with an inertial system with the advantage of an invariant filter structure. Specifically, to mitigate the nonlinearity of the propagation model and perturbing effect from the estimated uncertainty, the formulation of the invariant Kalman filter was derived in detail. Then, experiments were conducted to validate the algorithm with an unmanned vehicle equipped with an IMU and MPS receiver. As a result, the navigation performance of the IEKF-based inertial and magnetic field integration system was presented and compared with the conventional Kalman filter results. Furthermore, the convergence and navigation performance were evaluated in the presence of state variable initialization errors. The findings indicate that the inertial and magnetic field coupled with the IEKF outperforms the typical KF approach, particularly when dealing with initial estimate uncertainties.