Author name cluster

Neng Wang

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6 papers

2 author rows

IROS Conference 2025 Conference Paper

BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model

Ziyue Wang
Chenghao Shi
Neng Wang
Qinghua Yu
Xieyuanli Chen
Huimin Lu 0002

Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently, the emergence of end-to-end localization approaches has offered distinct advantages, including a streamlined system architecture and the elimination of the need to store extensive map data. Although these methods have demonstrated promising results, current end-to-end localization approaches still face limitations in robustness and accuracy. Bird’s-Eye-View (BEV) image is one of the most widely adopted data representations in autonomous driving. It significantly reduces data complexity while preserving spatial structure and scale consistency, making it an ideal representation for localization tasks. However, research on BEV-based end-to-end localization remains notably insufficient. To fill this gap, we propose BEVDiffLoc, a novel framework that formulates LiDAR localization as a conditional generation of poses. Leveraging the properties of BEV, we first introduce a specific data augmentation method to significantly enhance the diversity of input data. Then, the Maximum Feature Aggregation Module and Vision Transformer are employed to learn robust features while maintaining robustness against significant rotational view variations. Finally, we incorporate a diffusion model that iteratively refines the learned features to recover the absolute pose. Extensive experiments on the Oxford Radar RobotCar and NCLT datasets demonstrate that BEVDiffLoc outperforms the baseline methods. Our code is available at https://github.com/nubot-nudt/BEVDiffLoc.

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IROS Conference 2025 Conference Paper

Efficient Instance Motion-Aware Point Cloud Scene Prediction

Yiming Fang
Xieyuanli Chen
Neng Wang
Kaihong Huang
Huimin Lu 0002

Point cloud prediction (PCP) aims to forecast future 3D point clouds of scenes by leveraging sequential historical LiDAR scans, offering a promising avenue to enhance the perceptual capabilities of autonomous systems. However, existing methods mostly adopt an end-to-end approach without explicitly modeling moving instances, limiting their effectiveness in dynamic real-world environments. In this paper, we propose IMPNet, a novel instance motion-aware network for future point cloud scene prediction. Unlike prior works, IMPNet explicitly incorporates motion and instance-level information to enhance PCP accuracy. Specifically, we extract appearance and motion features from range images and residual images using a dual-branch convolutional network and fuse them via a motion attention block. Our framework further integrates a motion head for identifying moving objects and an instance-assisted training strategy to improve instance-wise point cloud predictions. Extensive experiments on multiple datasets demonstrate that our proposed network achieves state-of-the-art (SOTA) performance in PCP with superior predictive accuracy and robust generalization across diverse driving scenarios. Our method has been released at https://github.com/nubot-nudt/IMPNet.

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IROS Conference 2025 Conference Paper

Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM

Neng Wang
Huimin Lu 0002
Zhiqiang Zheng 0002
Yun-Hui Liu
Xieyuanli Chen

Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene representation that not only enhances localization accuracy in SLAM but also enables advanced cognitive functionalities for downstream navigation and planning tasks. Existing pointwise semantic LiDAR SLAM methods often suffer from poor efficiency and generalization, making them less robust in diverse real-world scenarios. In this paper, we propose a semantic graph-enhanced SLAM framework, named SG-SLAM, which effectively leverages the geometric, semantic, and topological characteristics inherent in environmental structures. The semantic graph serves as a fundamental component that facilitates critical functionalities of SLAM, including robust relocalization during odometry failures, accurate loop closing, and semantic graph map construction. Our method employs a dual-threaded architecture, with one thread dedicated to online odometry and relocalization, while the other handles loop closure, pose graph optimization, and map update. This design enables our method to operate in real time and generate globally consistent semantic graph maps and point cloud maps. We extensively evaluate our method across the KITTI, MulRAN, and Apollo datasets, and the results demonstrate its superiority compared to state-of-the-art methods. Our method has been released at https://github.com/nubot-nudt/SG-SLAM.

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EAAI Journal 2025 Journal Article

Sample less meta-learning fault diagnosis based on ordered time–frequency features

Cheng Wang
Neng Wang
Lili Deng

To address the challenges of insufficient fault samples and incomplete feature acquisition that compromise diagnostic accuracy, this study proposes a meta-learning framework for fault diagnosis integrating temporally-ordered time–frequency feature representation and a self-attention-enhanced multi-scale network. The proposed methodology systematically extracts time–frequency features from raw vibration signals and organizes them into discriminative two-dimensional (2D) image representations through structured temporal sequencing. A neural architecture combining spatial self-attention mechanisms with multi-scale convolutional feature extraction is developed, enhanced by meta-learning strategies to optimize parameter initialization. The model undergoes primary training on publicly available benchmark datasets to establish generalized feature representations, followed by task-specific fine-tuning and evaluation using targeted diagnostic datasets. Comprehensive experimental validation demonstrates the efficacy of the proposed approach, with the ordered time–frequency feature extraction achieving superior precision of 98. 28%. The trained network exhibits exceptional few-shot learning capabilities, and when only a single fault sample is available, it attains a maximum diagnostic accuracy of 81. 69%, which significantly outperforms conventional methods. Comparative analyses reveal enhanced adaptability and generalization capacity across diverse operational conditions, confirming the framework’s robustness in addressing data scarcity challenges inherent in industrial fault diagnosis scenarios.

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ICRA Conference 2024 Conference Paper

Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data

Kai Luan
Chenghao Shi
Neng Wang
Yuwei Cheng
Huimin Lu 0002
Xieyuanli Chen

The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatly limits the development of mmWave radar technology. In this paper, we propose a novel point cloud super-resolution approach for 3D mmWave radar data, named Radar-diffusion. Our approach employs the diffusion model defined by mean-reverting stochastic differential equations (SDE). Using our proposed new objective function with supervision from corresponding LiDAR point clouds, our approach efficiently handles radar ghost points and enhances the sparse mmWave radar point clouds to dense LiDAR-like point clouds. We evaluate our approach on two different datasets, and the experimental results show that our method outperforms the state-of-the-art baseline methods in 3D radar super-resolution tasks. Furthermore, we demonstrate that our enhanced radar point cloud is capable of downstream radar point-based registration tasks.

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IROS Conference 2023 Conference Paper

InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data

Neng Wang
Chenghao Shi
Ruibin Guo
Huimin Lu 0002
Zhiqiang Zheng 0002
Xieyuanli Chen

Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our approach not only predicts point-wise moving labels but also detects instance information of main traffic participants. Such a design helps determine which instances are actually moving and which ones are temporarily static in the current scene. Our method exploits a sequence of point clouds as input and quantifies them into 4D voxels. We use 4D sparse convolutions to extract motion features from the 4D voxels and inject them into the current scan. Then, we extract spatio-temporal features from the current scan for instance detection and feature fusion. Finally, we design an upsample fusion module to output point-wise labels by fusing the spatio-temporal features and predicted instance information. We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation. Furthermore, our method shows superior performance on the Apollo dataset with a pre-trained model on SemanticKITTI, indicating that our method generalizes well in different scenes. The code and pre-trained models of our method will be released at https://github.com/nubot-nudt/InsMOS.

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