arXiv subjects

Wenyi Liu

Publications and source records attributed to Wenyi Liu.

At least 19 recordsLinked to original sources

arXiv2026

Visibility-Aware Cooperative Tracking with Decentralized LiDAR-Based Aerial Swarms

Autonomous aerial tracking with drones offers vast potential for surveillance, cinematography, and industrial inspection applications. While single-drone tracking systems have been extensively studied, swarm-based target tracking remains underexplored, despite its unique advantages of distributed perception, fault-tolerant redundancy, and multidirectional target coverage. To bridge this gap, we propose a novel decentralized LiDAR-based swarm tracking framework that enables visibility-aware, cooperative target tracking in complex environments, while fully harnessing the unique capabilities of swarm systems. To address visibility, we introduce a novel Spherical Signed Distance Field (SSDF)-based metric for 3-D environmental occlusion representation, coupled with an efficient algorithm that enables real-time onboard SSDF updating. A general Field-of-View (FOV) alignment cost supporting heterogeneous LiDAR configurations is proposed for consistent target observation. Swarm coordination is enhanced through cooperative costs that enforce inter-robot safe clearance, prevent mutual occlusions, and notably facilitate 3-D multidirectional target encirclement via a novel electrostatic-potential-inspired distribution metric. These innovations are integrated into a hierarchical planner, combining a kinodynamic front-end searcher with a spatiotemporal $SE(3)$ back-end optimizer to generate collision-free, visibility-optimized trajectories.Deployed on heterogeneous LiDAR swarms, our fully decentralized implementation features collaborative perception, distributed planning, and dynamic swarm reconfigurability. Validated through rigorous real-world experiments in cluttered outdoor environments, the proposed system demonstrates robust cooperative tracking of agile targets (drones, humans) while achieving superior visibility maintenance.

cs.RO↗

arXiv2026

Pressure-Robust $H(\mathrm{div})$-Conforming HDG Methods for the Steady Stokes Equations with an Application to Tangential Boundary Control

We develop a family of $H(\mathrm{div})$-conforming hybridizable discontinuous Galerkin methods for the steady Stokes equations based on BDM and RT velocity spaces with either discontinuous or continuous hybrid traces. In contrast to our earlier pressure-robust HDG method for tangential boundary control, the present analysis does not require the pressure to belong to $H^1$; instead, the consistency argument only assumes low pressure regularity. The discrete velocities are exactly divergence-free, which yields pressure robustness. For the BDM variants we derive optimal energy-norm estimates and optimal $L^2$-velocity convergence, while for the RT variants we obtain optimal velocity convergence and weaker pressure estimates. We also analyze the hybridized linear system and prove a uniform spectral equivalence for the pressure Schur complement relevant to iterative solvers. As an application, we revisit the Stokes tangential boundary control problem and derive error estimates for the control, state, and adjoint variables using the BDM discontinuous-trace scheme. Two- and three-dimensional numerical experiments confirm the predicted convergence rates, the exact divergence-free property, and the robustness of the method with respect to the viscosity parameter.

math.NA↗

arXiv2026

2D Ferroelectric Ruddlesden-Popper Perovskites: an Emerging Fully Electronically Controllable Shift Current and Persistent Spin Helix

Two-dimensional (2D) hybrid organic--inorganic perovskites (HOIPs) are promising candidates for next-generation optoelectronic and spintronic applications. This work systematically investigates the relationship between structural distortions and functional responses in three $C_{2v}$-symmetric Ruddlesden--Popper (RP) ferroelectric perovskites, $(4,4\text{-DFPD})_{2}\mathrm{PbI}_{4}$, $(\mathrm{DFCHA})_{2}\mathrm{PbI}_{4}$, and PEPI, using first-principles calculations combined with irreducible representation decomposition and wave-vector point-group symmetry (WPGS) analysis. The results reveal that the lead--iodide framework yields shift-current (SC) magnitudes comparable to, and in specific cases even an order of magnitude larger than, those of traditional ferroelectric oxides, with PEPI reaching a maximum of $69.16\ μ\mathrm{A}/\mathrm{V}^{2}$. The SC magnitude correlates positively with the octahedral distortion index ($D_i$), while a competition mechanism is identified between covalent bond strength and structural asymmetry, where increased average bond lengths can offset the enhancement induced by $D_i$. Regarding spintronics, $C_{2v}$ symmetry-protected persistent spin textures (PST) are identified. A transition to $C_2$-protected quasi-PST occurs in monoclinic $(4,4\text{-DFHHA})_{2}\mathrm{PbI}_{4}$, leading to a persistent spin helix (PSH) with long-distance spin transport. The synergy among ferroelectricity, SC, and PST enables nonvolatile electrical control of both photocurrent direction and spin configurations. This work provides evaluation criteria and practical guidance for designing high-performance integrated spintronic--photovoltaic devices.

cond-mat.mtrl-sci↗

arXiv2026

Memory-Efficient Boundary Map for Large-Scale Occupancy Grid Mapping

Determining the occupancy status of locations in the environment is a fundamental task for safety-critical robotic applications. Traditional occupancy grid mapping methods subdivide the environment into a grid of voxels, each associated with one of three occupancy states: free, occupied, or unknown. These methods explicitly maintain all voxels within the mapped volume and determine the occupancy state of a location by directly querying the corresponding voxel that the location falls within. However, maintaining all grid voxels in high-resolution and large-scale scenarios requires substantial memory resources. In this paper, we introduce a novel representation that only maintains the boundary of the mapped volume. Specifically, we explicitly represent the boundary voxels, such as the occupied voxels and frontier voxels, while free and unknown voxels are automatically represented by volumes within or outside the boundary, respectively. As our representation maintains only a closed surface in two-dimensional (2D) space, instead of the entire volume in three-dimensional (3D) space, it significantly reduces memory consumption. Then, based on this 2D representation, we propose a method to determine the occupancy state of arbitrary locations in the 3D environment. We term this method as boundary map. Besides, we design a novel data structure for maintaining the boundary map, supporting efficient occupancy state queries. Theoretical analyses of the occupancy state query algorithm are also provided. Furthermore, to enable efficient construction and updates of the boundary map from the real-time sensor measurements, we propose a global-local mapping framework and corresponding update algorithms. Finally, we will make our implementation of the boundary map open-source on GitHub to benefit the community:https://github.com/hku-mars/BDM.

cs.RO↗

arXiv2025

Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency

Digital twin (DT) enables smart manufacturing by leveraging real-time data, AI models, and intelligent control systems. This paper presents a state-of-the-art analysis on the emerging field of DTs in the context of milling. The critical aspects of DT are explored through the lens of virtual models of physical milling, data flow from physical milling to virtual model, and feedback from virtual model to physical milling. Live data streaming protocols and virtual modeling methods are highlighted. A case study showcases the transformative capability of a real-time machine learning-driven live DT of tool-work contact in a milling process. Future research directions are outlined to achieve the goals of Industry 4.0 and beyond.

eess.SY↗

arXiv2025

Machine Learning-based Online Stability Lobe Diagram Estimation and Chatter Suppression Control in Milling Process

Chatter is a self-excited vibration in milling that degrades surface quality and accelerates tool wear. This paper presents an adaptive process controller that suppresses chatter by leveraging machine learning-based online estimation of the Stability Lobe Diagram (SLD) and surface roughness in the process. Stability analysis is conducted using the semi-discretization method for milling dynamics modeled by delay differential equations. An integrated machine learning framework estimates the SLD from sensor data and predicts surface roughness for chatter detection in real time. These estimates are integrated into an optimal controller that adaptively adjusts spindle speed to maintain process stability and improve surface finish. Simulations and experiments are performed to demonstrate the superior performance compared to the existing approaches.

eess.SY↗

arXiv2025

Flying through cluttered and dynamic environments with LiDAR

Navigating unmanned aerial vehicles (UAVs) through cluttered and dynamic environments remains a significant challenge, particularly when dealing with fast-moving or sudden-appearing obstacles. This paper introduces a complete LiDAR-based system designed to enable UAVs to avoid various moving obstacles in complex environments. Benefiting the high computational efficiency of perception and planning, the system can operate in real time using onboard computing resources with low latency. For dynamic environment perception, we have integrated our previous work, M-detector, into the system. M-detector ensures that moving objects of different sizes, colors, and types are reliably detected. For dynamic environment planning, we incorporate dynamic object predictions into the integrated planning and control (IPC) framework, namely DynIPC. This integration allows the UAV to utilize predictions about dynamic obstacles to effectively evade them. We validate our proposed system through both simulations and real-world experiments. In simulation tests, our system outperforms state-of-the-art baselines across several metrics, including success rate, time consumption, average flight time, and maximum velocity. In real-world trials, our system successfully navigates through forests, avoiding moving obstacles along its path.

cs.RO↗

arXiv2025

LiDAR-based Quadrotor Autonomous Inspection System in Cluttered Environments

In recent years, autonomous unmanned aerial vehicle (UAV) technology has seen rapid advancements, significantly improving operational efficiency and mitigating risks associated with manual tasks in domains such as industrial inspection, agricultural monitoring, and search-and-rescue missions. Despite these developments, existing UAV inspection systems encounter two critical challenges: limited reliability in complex, unstructured, and GNSS-denied environments, and a pronounced dependency on skilled operators. To overcome these limitations, this study presents a LiDAR-based UAV inspection system employing a dual-phase workflow: human-in-the-loop inspection and autonomous inspection. During the human-in-the-loop phase, untrained pilots are supported by autonomous obstacle avoidance, enabling them to generate 3D maps, specify inspection points, and schedule tasks. Inspection points are then optimized using the Traveling Salesman Problem (TSP) to create efficient task sequences. In the autonomous phase, the quadrotor autonomously executes the planned tasks, ensuring safe and efficient data acquisition. Comprehensive field experiments conducted in various environments, including slopes, landslides, agricultural fields, factories, and forests, confirm the system's reliability and flexibility. Results reveal significant enhancements in inspection efficiency, with autonomous operations reducing trajectory length by up to 40\% and flight time by 57\% compared to human-in-the-loop operations. These findings underscore the potential of the proposed system to enhance UAV-based inspections in safety-critical and resource-constrained scenarios.

cs.RO↗

arXiv2025

SLIM: Scalable and Lightweight LiDAR Mapping in Urban Environments

LiDAR point cloud maps are extensively utilized on roads for robot navigation due to their high consistency. However, dense point clouds face challenges of high memory consumption and reduced maintainability for long-term operations. In this study, we introduce SLIM, a scalable and lightweight mapping system for long-term LiDAR mapping in urban environments. The system begins by parameterizing structural point clouds into lines and planes. These lightweight and structural representations meet the requirements of map merging, pose graph optimization, and bundle adjustment, ensuring incremental management and local consistency. For long-term operations, a map-centric nonlinear factor recovery method is designed to sparsify poses while preserving mapping accuracy. We validate the SLIM system with multi-session real-world LiDAR data from classical LiDAR mapping datasets, including KITTI, NCLT, HeLiPR and M2DGR. The experiments demonstrate its capabilities in mapping accuracy, lightweightness, and scalability. Map re-use is also verified through map-based robot localization. Finally, with multi-session LiDAR data, the SLIM system provides a globally consistent map with low memory consumption (~130 KB/km on KITTI).

cs.RO↗

arXiv2024

Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing

This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network hierarchy deepens. The proposed approach introduces three GNN-based recommendation models, specifically designed to mitigate over-smoothing through innovative mechanisms like residual connections and identity mapping within the aggregation propagation process. These modifications enable more effective information flow across layers, preserving essential user-item interaction details to improve recommendation accuracy. Additionally, the study emphasizes the critical need for interpretability in recommendation systems, aiming to provide transparent and justifiable suggestions tailored to dynamic user preferences. By integrating collaborative filtering with GNN architectures, the proposed models not only enhance predictive accuracy but also align recommendations more closely with individual behaviors, adapting to nuanced shifts in user interests. This work advances the field by tackling both technical and user-centric challenges, contributing to the development of robust and explainable recommendation systems capable of managing the complexity and scale of modern online environments.

cs.IR↗

arXiv2024

Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues

With the rapid growth of digital information, personalized recommendation systems have become an indispensable part of Internet services, especially in the fields of e-commerce, social media, and online entertainment. However, traditional collaborative filtering and content-based recommendation methods have limitations in dealing with data sparsity and cold start problems, especially in the face of largescale heterogeneous data, which makes it difficult to meet user expectations. This paper proposes a new label recommendation algorithm based on metric learning, which aims to overcome the challenges of traditional recommendation systems by learning effective distance or similarity metrics to capture the subtle differences between user preferences and item features. Experimental results show that the algorithm outperforms baseline methods including local response metric learning (LRML), collaborative metric learning (CML), and adaptive tensor factorization (ATF) based on adversarial learning on multiple evaluation metrics. In particular, it performs particularly well in the accuracy of the first few recommended items, while maintaining high robustness and maintaining high recommendation accuracy.

cs.IR↗

arXiv2024

Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks

This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional GNN methods may be overly dependent on the initial structure and attribute information of the graph, which limits their ability to accurately simulate more complex relationships and patterns in the graph. Therefore, this study proposes a graph neural network model under a self-supervised learning framework, which can flexibly combine different types of additional information of the attribute graph and its nodes, so as to better mine the deep features in the graph data. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data and improve the overall performance of the model.

cs.LG↗

arXiv2024

A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining

With the explosive growth of Internet data, users are facing the problem of information overload, which makes it a challenge to efficiently obtain the required resources. Recommendation systems have emerged in this context. By filtering massive amounts of information, they provide users with content that meets their needs, playing a key role in scenarios such as advertising recommendation and product recommendation. However, traditional click-through rate prediction and TOP-K recommendation mechanisms are gradually unable to meet the recommendations needs in modern life scenarios due to high computational complexity, large memory consumption, long feature selection time, and insufficient feature interaction. This paper proposes a recommendations system model based on a separation embedding cross-network. The model uses an embedding neural network layer to transform sparse feature vectors into dense embedding vectors, and can independently perform feature cross operations on different dimensions, thereby improving the accuracy and depth of feature mining. Experimental results show that the model shows stronger adaptability and higher prediction accuracy in processing complex data sets, effectively solving the problems existing in existing models.

cs.IR↗

arXiv2024

Voxel-SLAM: A Complete, Accurate, and Versatile LiDAR-Inertial SLAM System

In this work, we present Voxel-SLAM: a complete, accurate, and versatile LiDAR-inertial SLAM system that fully utilizes short-term, mid-term, long-term, and multi-map data associations to achieve real-time estimation and high precision mapping. The system consists of five modules: initialization, odometry, local mapping, loop closure, and global mapping, all employing the same map representation, an adaptive voxel map. The initialization provides an accurate initial state estimation and a consistent local map for subsequent modules, enabling the system to start with a highly dynamic initial state. The odometry, exploiting the short-term data association, rapidly estimates current states and detects potential system divergence. The local mapping, exploiting the mid-term data association, employs a local LiDAR-inertial bundle adjustment (BA) to refine the states (and the local map) within a sliding window of recent LiDAR scans. The loop closure detects previously visited places in the current and all previous sessions. The global mapping refines the global map with an efficient hierarchical global BA. The loop closure and global mapping both exploit long-term and multi-map data associations. We conducted a comprehensive benchmark comparison with other state-of-the-art methods across 30 sequences from three representative scenes, including narrow indoor environments using hand-held equipment, large-scale wilderness environments with aerial robots, and urban environments on vehicle platforms. Other experiments demonstrate the robustness and efficiency of the initialization, the capacity to work in multiple sessions, and relocalization in degenerated environments.

cs.RO↗

arXiv2024

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains.

cs.LG↗

arXiv2024

Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms

Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, search and rescue. Efficient, accurate self and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This paper proposes Swarm-LIO2: a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient LiDAR-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized, plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego-state, mutual observation measurements, and global extrinsic transformations. To support the plug-and-play of new teammate participants, Swarm-LIO2 detects potential teammate UAVs and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based UAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, IMU, and mutual observation measurements within an efficient ESIKF framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency.

cs.RO↗

arXiv2024

LiDAR-based Quadrotor for Slope Inspection in Dense Vegetation

This work presents a LiDAR-based quadrotor system for slope inspection in dense vegetation environments. Cities like Hong Kong are vulnerable to climate hazards, which often result in landslides. To mitigate the landslide risks, the Civil Engineering and Development Department (CEDD) has constructed steel flexible debris-resisting barriers on vulnerable natural catchments to protect residents. However, it is necessary to carry out regular inspections to identify any anomalies, which may affect the proper functioning of the barriers. Traditional manual inspection methods face challenges and high costs due to steep terrain and dense vegetation. Compared to manual inspection, unmanned aerial vehicles (UAVs) equipped with LiDAR sensors and cameras have advantages such as maneuverability in complex terrain, and access to narrow areas and high spots. However, conducting slope inspections using UAVs in dense vegetation poses significant challenges. First, in terms of hardware, the overall design of the UAV must carefully consider its maneuverability in narrow spaces, flight time, and the types of onboard sensors required for effective inspection. Second, regarding software, navigation algorithms need to be designed to enable obstacle avoidance flight in dense vegetation environments. To overcome these challenges, we develop a LiDAR-based quadrotor, accompanied by a comprehensive software system. The goal is to deploy our quadrotor in field environments to achieve efficient slope inspection. To assess the feasibility of our hardware and software system, we conduct functional tests in non-operational scenarios. Subsequently, invited by CEDD, we deploy our quadrotor in six field environments, including five flexible debris-resisting barriers located in dense vegetation and one slope that experienced a landslide. These experiments demonstrated the superiority of our quadrotor in slope inspection.

cs.RO↗

arXiv2022

Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors

Quadrotors are agile platforms. With human experts, they can perform extremely high-speed flights in cluttered environments. However, fully autonomous flight at high speed remains a significant challenge. In this work, we propose a motion planning algorithm based on the corridor-constrained minimum control effort trajectory optimization (MINCO) framework. Specifically, we use a series of overlapping spheres to represent the free space of the environment and propose two novel designs that enable the algorithm to plan high-speed quadrotor trajectories in real-time. One is a sampling-based corridor generation method that generates spheres with large overlapped areas (hence overall corridor size) between two neighboring spheres. The second is a Receding Horizon Corridors (RHC) strategy, where part of the previously generated corridor is reused in each replan. Together, these two designs enlarge the corridor spaces in accordance with the quadrotor's current state and hence allow the quadrotor to maneuver at high speeds. We benchmark our algorithm against other state-of-the-art planning methods to show its superiority in simulation. Comprehensive ablation studies are also conducted to show the necessity of the two designs. The proposed method is finally evaluated on an autonomous LiDAR-navigated quadrotor UAV in woods environments, achieving flight speeds over 13.7 m/s without any prior map of the environment or external localization facility.

cs.RO↗