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Guijin Wang

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4 papers

2 author rows

IROS Conference 2025 Conference Paper

FEG-VON: Frontier Embedding Graph for Efficient Visual Object Navigation

Yingru Dai
Pengwei Xie
Yikai Liu
Siang Chen
Wenming Yang
Guijin Wang

Visual object navigation, requiring agents to locate target objects in novel environments through egocentric visual observation, remains a critical challenge in Embodied AI. We propose FEG-VON, a training-free framework that constructs and maintains a Frontier Embedding Graph for efficient Visual Object Navigation. The graph initializes frontier embeddings using Vision Language Models (VLMs), where visual observations are encoded into spatially anchored semantic embeddings through cross-modal alignment with target text descriptors. We then update the graph by aggregating spatio-temporal semantic relations across frontiers, enabling online adaptation to new targets via similarity scoring without remapping. The evaluation results in public benchmarks demonstrate the superior performance of FEG-VON in both single- and multi-object navigation tasks compared with state-of-the-art methods. Crucially, FEG-VON eliminates dependency on task-specific training for exploration and advances the feasibility of zero-shot navigation in open-world environments.

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IROS Conference 2025 Conference Paper

Region-Centric 6-Dof Grasp Detection: A Data-Efficient Solution for Cluttered Scenes

Siang Chen
Wei Tang
Pengwei Xie
Dingchang Hu
Wenming Yang
Guijin Wang

Robotic grasping, serving as the cornerstone of robot manipulation, is fundamental for embodied intelligence. Manipulation in challenging scenarios demands grasp detection algorithms with higher efficiency and generalizability. However, for general 6-Dof grasp detection, most data-driven methods directly extract scene-level features to generate grasp prediction, relying on a relatively heavy scene-level feature encoder and a significant amount of data with dense grasp labels for model training. In this letter, we propose a novel data-efficient 6-Dof grasp detection framework in cluttered scenes, named Region-Centric Grasp Detection (RCGD), consisting of an Iterative Search Module (ISM) and a Region Grasp Model (RGM). Concretely, ISM aims to retrieve potential region centers and aggregate multiple regions in a coarse-to-fine way. Then, RGM extracts aligned grasp-related embeddings and predicts grasps within these local regions. Benefiting from the region-centric paradigm and the training-free location strategy, RCGD significantly outperforms previous methods and shows minimal performance loss with even a very small portion of training data or labels. Furthermore, real-world robotic experiments in two distinct settings highlight the effectiveness of our method with a 95% success rate.

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ICRA Conference 2025 Conference Paper

SAP-SLAM: Semantic-Assisted Perception SLAM with 3D Gaussian Splatting

Yuheng Yang
Yudong Lin
Wenming Yang
Guijin Wang
Qingmin Liao

The integration of 3D Gaussians has introduced a novel scene representation in Simultaneous Localization and Mapping (SLAM), characterized by explicit representation and differentiable rendering capabilities that enhance scene reconstruction and understanding. However, most current SLAM systems only exploit the basic representational capacity of 3D Gaussians, neglecting their potential to offer richer information and facilitate higher-dimensional scene comprehension. Furthermore, these systems often struggle with reconstruction when encountering rapid camera movements or depth missing. Drawing inspiration from 3D language field, which explores the intrinsic relationships among scene objects, we propose SAPSLAM, a dense SLAM system that combines high-fidelity reconstruction and advanced semantic understanding. Our approach leverages pre-trained visual models to extract semantic features, which are then fused, dimensionally reduced, and encoded into the 3D Gaussian model for optimization and rendering. The integration of these features improves the systems semantic comprehension and scene representation, ultimately enabling the creation of high-precision 3D semantic maps. Additionally, we introduce a semantic-guided Gaussian densification and pruning strategy, which uses semantic consistency to prioritize attention on poorly reconstructed areas, greatly improving performance in complex scenarios. SAP-SLAM achieves competitive results on both real-world and synthetic datasets, demonstrating superior capabilities in semantic understanding and reconstruction.

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AAAI Conference 2022 Conference Paper

Pose-Invariant Face Recognition via Adaptive Angular Distillation

Zhenduo Zhang
Yongru Chen
Wenming Yang
Guijin Wang
Qingmin Liao

Pose-invariant face recognition is a practically useful but challenging task. This paper introduces a novel method to learn pose-invariant feature representation without normalizing profile faces to frontal ones or learning disentangled features. We first design a novel strategy to learn pose-invariant feature embeddings by distilling the angular knowledge of frontal faces extracted by teacher network to student network, which enables the handling of faces with large pose variations. In this way, the features of faces across variant poses can cluster compactly for the same person to create a poseinvariant face representation. Secondly, we propose a Pose- Adaptive Angular Distillation loss to mitigate the negative effect of uneven distribution of face poses in the training dataset to pay more attention to the samples with large pose variations. Extensive experiments on two challenging benchmarks (IJB-A and CFP-FP) show that our approach consistently outperforms the existing methods.

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