Tiantian Dong

This study presents a LiDAR--Inertial--Visual Odometry framework that maintains robust state estimation under perceptual degradation. Building on a tightly coupled Error-State Iterated Kalman Filter (ESIKF), the method integrates modality-wise dual-confidence estimation with explicit degradation handling. A deep learning--based visual front end improves feature correspondence under illumination variations and texture-scarce scenes. During the measurement update, visual confidence is derived from the number of correspondences and reprojection error, while LiDAR confidence is estimated from point-to-plane residual statistics and the ratio of effective geometric features. These confidence scores are incorporated via dynamic covariance scaling to adaptively allocate modality contributions. When a modality becomes severely degraded, a degradation-aware transition policy regulates its influence through covariance inflation for soft suppression and temporary attenuation under persistent degradation, with a conservative recovery criterion to avoid frequent toggling. If a modality switch is required, poses are smoothly blended over a short transition window to preserve trajectory continuity and improve estimation stability. Experiments on NTU-VIRAL and Hilti'22 demonstrate competitive accuracy and consistent trajectories in degraded conditions, validating the robustness of the proposed framework.

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.

This paper addresses the issue of dynamic instability in quadrotor caused by changes in load mass during flight. To tackle this problem, the Lyapunov exponent method is adopted to study the dynamics and motion stability of the system. This approach resolves the challenge of constructing system eigenvalues due to the nonlinearity and high order of the quadrotor. To enhance the reliability of stability analysis, a quantitative relationship between system dynamics parameters and motion stability is established by combining the dynamic model with the Lyapunov exponent method. This approach compensates for inaccuracies in theoretical modeling analysis caused by factors such as load mass changes. The experiments demonstrate that changing the wheelbase and load mass improves flight motion stability, ensuring the reliability of the quadrotor flight system. Overall, this paper provides an in-depth analysis of the motion stability of a quadrotor and proposes a reliable method for stability analysis that accounts for changes in load mass during flight.