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Lingyun Xu

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation

Zekun Qi
Wenyao Zhang
Yufei Ding
Runpei Dong
XinQiang Yu
Jingwen Li
Lingyun Xu
Baoyu Li

While spatial reasoning has made progress in object localization relationships, it often overlooks object orientation—a key factor in 6-DoF fine-grained manipulation. Traditional pose representations rely on pre-defined frames or templates, limiting generalization and semantic grounding. In this paper, we introduce the concept of semantic orientation, which defines object orientations using natural language in a reference-frame-free manner (e. g. , the ''plug-in'' direction of a USB or the ''handle'' direction of a cup). To support this, we construct OrienText300K, a large-scale dataset of 3D objects annotated with semantic orientations, and develop PointSO, a general model for zero-shot semantic orientation prediction. By integrating semantic orientation into VLM agents, our SoFar framework enables 6-DoF spatial reasoning and generates robotic actions. Extensive experiments demonstrated the effectiveness and generalization of our SoFar, e. g. , zero-shot 48. 7\% successful rate on Open6DOR and zero-shot 74. 9\% successful rate on SIMPLER-Env.

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ICRA Conference 2019 Conference Paper

A Multi-Domain Feature Learning Method for Visual Place Recognition

Peng Yin 0001
Lingyun Xu
Xueqian Li
Chen Yin
Yingli Li
Rangaprasad Arun Srivatsan
Lu Li
Jianmin Ji

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the environmental condition-related features from those that are not. The only label required within this feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant environmental conditions.

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ICRA Conference 2019 Conference Paper

MRS-VPR: a multi-resolution sampling based global visual place recognition method

Peng Yin 0001
Rangaprasad Arun Srivatsan
Yin Chen
Xueqian Li
Hongda Zhang
Lingyun Xu
Lu Li
Zhenzhong Jia

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.

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ICRA Conference 2018 Conference Paper

A Failure-Tolerant Approach to Synchronous Formation Control of Mobile Robots Under Communication Delays

Zhe Liu 0022
Hesheng Wang 0001
Lingyun Xu
Yun-Hui Liu 0001
Jun-Guo Lu
Weidong Chen 0001

Robot malfunction is inevitable in practical applications of the robot formation control due to uncontrolled crashing, system malfunction or communication loss. In this paper, we study the synchronous formation control problem in the presence of robot malfunctions. Our main idea is to improve the network connectivity and motion synchronism of the robot formation through a series of topology switchings and robot replacements. Firstly, the synchronous formation control method is introduced which enables the robots to tracking their desired trajectories while keeping predefined formation shapes. Secondly, a recursive switched topology control strategy is proposed to restore the formation shape as well as to improve the network connectivity and motion synchronism in the presence of robot malfunctions. Thirdly, the convergence analysis of the proposed control system is presented and a sufficient condition is obtained under an average dwell time scheme. What's more, the proposed approach is fully distributed and the communication delays between neighboring robots also have been taken into consideration. Simulation results demonstrate the effectiveness of the proposed approach.

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IROS Conference 2018 Conference Paper

Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition

Peng Yin 0001
Lingyun Xu
Zhe Liu 0022
Lu Li
Hadi Salman
Yuqing He
Weiliang Xu 0001
Hesheng Wang 0001

Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot's motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What's more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.

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