Author name cluster

Weidong Chen

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7 papers

2 author rows

IROS Conference 2025 Conference Paper

A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points

Chuan Cao
Xiaoning Wang
Wenqian Xi
Han Zhang
Weidong Chen
Jingchuan Wang

4D imaging radar is a type of low-cost millimeter-wave radar(costing merely 10-20% of lidar systems) capable of providing range, azimuth, elevation, and Doppler velocity information. Accurate extrinsic calibration between millimeter-wave radar and camera systems is critical for robust multimodal perception in robotics, yet remains challenging due to inherent sensor noise characteristics and complex error propagation. This paper presents a systematic calibration framework to address critical challenges through a spatial 3d uncertainty-aware PnP algorithm (3DUPnP) that explicitly models spherical coordinate noise propagation in radar measurements, then compensating for non-zero error expectations during coordinate transformations. Finally, experimental validation demonstrates significant performance improvements over state-of-the-art CPnP baseline, including improved consistency in simulations and enhanced precision in physical experiments. This study provides a robust calibration solution for robotic systems equipped with millimeter-wave radar and cameras, tailored specifically for autonomous driving and robotic perception applications.

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AAAI Conference 2025 Conference Paper

Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection

Xiaoyu Huang
Weidong Chen
Bo Hu
Zhendong Mao

Multivariate time series (MTS) anomaly detection is a critical task that involves identifying abnormal patterns or events in data that consist of multiple interrelated time series. In order to better model the complex interdependence between entities and the various inherent characteristics of each entity, the graph neural network (GNN) based methods are widely adopted by existing methods. In each layer of GNN, node features aggregate information from their neighboring nodes to update their information. In doing so, from shallow layer to deep layer in GNN, original individual node features continue to be weakened and more structural information, i.e., from short-distance neighborhood to long-distance neighborhood, continues to be enhanced. However, research to date has largely ignored the understanding of how hierarchical graph information is represented and their characteristics that can benefit anomaly detection. Existing methods simply leverage the output from the last layer of GNN for anomaly estimation while neglecting the essential information contained in the intermediate GNN layers. To address such limitations, in this paper, we propose a Graph Mixture of Experts (Graph-MoE) network for multivariate time series anomaly detection, which incorporates the mixture of experts (MoE) module to adaptively represent and integrate hierarchical multi-layer graph information into entity representations. It is worth noting that our Graph-MoE can be integrated into any GNN-based MTS anomaly detection method in a plug-and-play manner. In addition, the memory-augmented routers are proposed in this paper to capture the correlation temporal information in terms of the global historical features of MTS to adaptively weigh the obtained entity representations to achieve successful anomaly estimation. Extensive experiments on five challenging datasets prove the superiority of our approach and each proposed module.

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IROS Conference 2025 Conference Paper

SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis

Yi Chen
Tianchen Deng
Wentao Zhao
Xiaoning Wang
Wenqian Xi
Weidong Chen
Jingchuan Wang

Recent research has begun exploring novel view synthesis (NVS) for LiDAR point clouds, aiming to generate realistic LiDAR scans from unseen viewpoints. However, most existing approaches do not reconstruct semantic labels, which are crucial for many downstream applications such as autonomous driving and robotic perception. Unlike images, which benefit from powerful segmentation models, LiDAR point clouds lack such large-scale pre-trained models, making semantic annotation time-consuming and labor-intensive. To address this challenge, we propose SN-LiDAR, a method that jointly performs accurate semantic segmentation, high-quality geometric reconstruction, and realistic LiDAR synthesis. Specifically, we employ a coarse-to-fine planar-grid feature representation to extract global features from multi-frame point clouds and leverage a CNN-based encoder to extract local semantic features from the current frame point cloud. Extensive experiments on SemanticKITTI and KITTI-360 demonstrate the superiority of SN-LiDAR in both semantic and geometric reconstruction, effectively handling dynamic objects and large-scale scenes. Codes will be available on https://github.com/dtc111111/SN-Lidar.

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AAAI Conference 2024 Conference Paper

Bootstrapping Large Language Models for Radiology Report Generation

Chang Liu
Yuanhe Tian
Weidong Chen
Yan Song
Yongdong Zhang

Radiology report generation (RRG) aims to automatically generate a free-text description from a specific clinical radiograph, e.g., chest X-Ray images. Existing approaches tend to perform RRG with specific models trained on the public yet limited data from scratch, where they often lead to inferior performance owing to the problem of inefficient capabilities in both aligning visual and textual features and generating informative reports accordingly. Currently, large language models (LLMs) offered a promising solution to text generation with their power in learning from big data, especially for cross-modal scenarios such as RRG. However, most existing LLMs are pre-trained on general data, and suffer from the same problem of conventional approaches caused by knowledge gap between general and medical domain if they are applied to RRG. Therefore in this paper, we propose an approach to bootstrapping LLMs for RRG with a in-domain instance induction and a coarse-to-fine decoding process. Specifically, the in-domain instance induction process learns to align the LLM to radiology reports from general texts through contrastive learning. The coarse-to-fine decoding performs a text elevating process for those reports from the ranker, further enhanced with visual features and refinement prompts. Experimental results on two prevailing RRG datasets, namely, IU X-Ray and MIMIC-CXR, demonstrate the superiority of our approach to previous state-of-the-art solutions. Further analyses illustrate that, for the LLM, the induction process enables it to better align with the medical domain and the coarse-to-fine generation allows it to conduct more precise text generation.

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ICRA Conference 2011 Conference Paper

Hybrid map-based navigation for intelligent wheelchair

Yong Wang
Weidong Chen

A navigation system based on hybrid map for intelligent wheelchair is presented. The system is consisted of hybrid map building, localization, path planning and trajectory following. The hybrid map includes a series of small probabilistic grid maps (PGM) and a global topological map (GTM). They are built simultaneously and easily using the human-guided method. Then on the hybrid map, the localization and the real-time path planning algorithms are realized smartly and effectively. The experiments and applications results show that the human-guided method integrates both the computer's modeling ability and the human's sensory perception to the environment. The hybrid map is easy to solve the loop-closure and doorway problems that enhances the robustness against uncertainty of sensors. It also can improve the efficiency in large-scale SLAM. Further more, at an elderly home we did the activities of daily living (ADL) testing and at Shanghai Expo 2010 we demonstrated the wheelchair system by offering trial rides to visitors.

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ICRA Conference 2009 Conference Paper

Sparsing of information matrix for practical application of a robot's SLAM

Haiwei Dong 0001
Zhiwei Luo
Weidong Chen

Mobile robot could navigate in unknown environment autonomously with the help of simultaneous localization and mapping (SLAM). Recently, SLAM based on information matrix enjoys much popularity since it is naturally sparse. However, the computational burden related to information matrix balloons with respect to the increase of the mapped landmarks. In this paper, by considering the features of information matrix, we present a novel method which wipes off nearly half of the elements in information matrix. The errors that come from sparsification decrease apparently by loop-closure. Furthermore, the relationship between sparsification and SLAM accuracy is analyzed theoretically. A large scale simulation and experiment conducted on a real robot suggest that the technique is effective for a robot's SLAM in real-world applications.

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EAAI Journal 2008 Journal Article

Adaptive neural-fuzzy control of uncertain constrained multiple coordinated nonholonomic mobile manipulators

Zhijun Li
Weidong Chen

Most studies on the coordination of multiple mobile manipulators system assume exact knowledge of system kinematics and dynamics, and deal only with motion tracking control. However, actual applications may involve tasks in which multiple coordinated mobile manipulators system is required to keep contact on the contour of the constraint surface in tasks. In this paper, we consider multiple mobile manipulators grasping a rigid object in contact with deformable working surfaces, whose geometric and physical model is unknown. The contact forces are nonlinear and unknown. Adaptive neuro-fuzzy (NF) control for coordinated mobile manipulators is proposed for robust force/motion tracking on the constraint surface while it is in motion. The control law is decoupled in three subspaces and adaptive tuning mechanism is developed to deal with the uncertain environmental constraints, disturbances, and unknown robotic dynamics. The proposed adaptive NF hybrid force/motion controller guarantees robust tracking of the desired motion and force trajectories. Simulation examples are presented to illustrate the results.

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