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Guangming Wang

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5 papers

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AAAI Conference 2026 Conference Paper

CoMA-SLAM: Collaborative Multi-Agent Gaussian SLAM with Geometric Consistency

Lin Chen
Yongxin Su
Jvboxi Wang
Pengcheng Han
Zhenyu Xia
Shuhui Bu
Kun Li
Boni Hu

Although Gaussian scene representation has achieved remarkable success in tracking and mapping, most existing methods are confined to single-agent systems. Current multi-agent solutions typically rely on centralized architectures, which struggle to account for communication bandwidth constraints. Furthermore, the inherent depth ambiguity of 3D Gaussian splatting poses notable challenges in maintaining geometric consistency. To address these challenges, we introduce CoMA-SLAM, the first distributed multi-agent Gaussian SLAM framework. By leveraging 2D Gaussian surfels and robust initialization strategy, CoMA-SLAM enhances tracking accuracy and geometry consistency. It efficiently manages communication bandwidth while dynamically scaling with the number of agents. Through the integration of intra- and inter-loop closure, distributed keyframe optimization and submap centric update, our framework ensures global consistency and robustly alignment. Synthetic and real-world experiments demonstrate that CoMA-SLAM outperforms state-of-the-art methods in pose accuracy, rendering fidelity, and geometric consistency while maintaining competitive efficiency across distributed multi-agent systems. Notably, by avoiding data transmission to a centralized server, our method reduces communication bandwidth by 99.8% compared to centralized approaches.

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JBHI Journal 2024 Journal Article

A Feature Fusion Model Based on Temporal Convolutional Network for Automatic Sleep Staging Using Single-Channel EEG

Jiameng Bao
Guangming Wang
Tianyu Wang
Ning Wu
Shimin Hu
Won Hee Lee
Sio-Long Lo
Xiangguo Yan

Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we proposed a novel deep learning algorithm named feature fusion temporal convolutional network (FFTCN) for automatic sleep staging using single-channel EEG data. This algorithm employed a one-dimensional convolutional neural network (1D-CNN) to extract temporal features from raw EEG, and a two-dimensional CNN (2D-CNN) to extract time-frequency features from spectrograms generated through continuous wavelet transform (CWT) at the epoch level. These features were subsequently fused and further fed into a temporal convolutional network (TCN) to classify sleep stages at the sequence level. Moreover, a two-step training strategy was used to enhance the model's performance on an imbalanced dataset. Our proposed method exhibits superior performance in the 5-class classification task for healthy subjects, as evaluated on the SHHS-1, Sleep-EDF-153, and ISRUC-S1 datasets. This work provided a straightforward and promising method for improving the accuracy of automatic sleep staging using only single-channel EEG, and the proposed method exhibited great potential for future applications in professional sleep monitoring, which could effectively alleviate the workload of sleep technicians.

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NeurIPS Conference 2024 Conference Paper

Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation

Yu Zheng
Guangming Wang
Jiuming Liu
Marc Pollefeys
Hesheng Wang

LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Recently, many works project the point cloud onto the 2D image and adopt the 2D Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point cloud semantic segmentation. However, since more than one point can be projected onto the same 2D position but only one point can be preserved, the previous 2D projection-based segmentation methods suffer from inevitable quantized information loss, which results in incomplete geometric structure, especially for small objects. To avoid quantized information loss, in this paper, we propose a novel spherical frustum structure, which preserves all points projected onto the same 2D position. Additionally, a hash-based representation is proposed for memory-efficient spherical frustum storage. Based on the spherical frustum structure, the Spherical Frustum sparse Convolution (SFC) and Frustum Farthest Point Sampling (F2PS) are proposed to convolve and sample the points stored in spherical frustums respectively. Finally, we present the Spherical Frustum sparse Convolution Network (SFCNet) to adopt 2D CNNs for LiDAR point cloud semantic segmentation without quantized information loss. Extensive experiments on the SemanticKITTI and nuScenes datasets demonstrate that our SFCNet outperforms previous 2D projection-based semantic segmentation methods based on conventional spherical projection and shows better performance on small object segmentation by preserving complete geometric structure. Codes will be available at https: //github. com/IRMVLab/SFCNet.

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EAAI Journal 2023 Journal Article

Fault diagnosis of power-shift system in continuously variable transmission tractors based on improved echo state network

Guangming Wang
Lijun Xue
Youfeng Zhu
Yehui Zhao
Honghua Jiang
Jinxing Wang

For better reliability of tractors with continuously variable transmission, reported here is fault diagnosis of their power-shift systems. First, four hydraulic system faults are analyzed, i. e. , pipe leakage, pipe blockage, a stuck solenoid valve spool, and a stuck clutch piston, and it is shown that these lead to clutch energy loss during power shifting and possibly even clutch burn-out. Second, fault simulations give more than 30000 groups of test data, and an improved dynamic time warping algorithm is used to segment the data samples automatically. Third, an improved echo state network is proposed to classify the above fault samples, and its performance is compared with those of traditional algorithms. Finally, the robustness of the proposed algorithm is tested using samples with noise superimposed. The results show that the accuracy, precision, recall, and F1-score of the improved echo state network are 96. 87%, 96. 31%, 97. 11%, and 96. 71%, respectively, making it equivalent in performance to a convolutional neural network with long short-term memory. However, the training speed of the former is significantly better than that of the latter, and even under the influence of strong noise, the proposed algorithm can still achieve an accuracy of 91. 32%, which is better than the latter’s 86. 62%. This is significantly better than traditional algorithms, thus demonstrating the better generalization characteristics and robustness of the proposed approach.

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AAAI Conference 2023 Conference Paper

TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry

Jiuming Liu
Guangming Wang
Chaokang Jiang
Zhe Liu
Hesheng Wang

Recently, transformer architecture has gained great success in the computer vision community, such as image classification, object detection, etc. Nonetheless, its application for 3D vision remains to be explored, given that point cloud is inherently sparse, irregular, and unordered. Furthermore, existing point transformer frameworks usually feed raw point cloud of N×3 dimension into transformers, which limits the point processing scale because of their quadratic computational costs to the input size N. In this paper, we rethink the structure of point transformer. Instead of directly applying transformer to points, our network (TransLO) can process tens of thousands of points simultaneously by projecting points onto a 2D surface and then feeding them into a local transformer with linear complexity. Specifically, it is mainly composed of two components: Window-based Masked transformer with Self Attention (WMSA) to capture long-range dependencies; Masked Cross-Frame Attention (MCFA) to associate two frames and predict pose estimation. To deal with the sparsity issue of point cloud, we propose a binary mask to remove invalid and dynamic points. To our knowledge, this is the first transformer-based LiDAR odometry network. The experiment results on the KITTI odometry dataset show that our average rotation and translation RMSE achieves 0.500°/100m and 0.993% respectively. The performance of our network surpasses all recent learning-based methods and even outperforms LOAM on most evaluation sequences.Codes will be released on https://github.com/IRMVLab/TransLO.

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