Author name cluster

Lin Chen 0042

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IROS Conference 2025 Conference Paper

CODE: COllaborative Visual-UWB SLAM for Online Large-Scale Metric DEnse Mapping

Lin Chen 0042
Xuan Jia
Shuhui Bu
Guangming Wang 0001
Kun Li
Zhenyu Xia
Xiaohan Li
Pengcheng Han

This paper presents a novel collaborative online dense mapping system for multiple Unmanned Aerial Vehicles (UAVs). The system confers two primary benefits: it facilitates simultaneous UAVs co-localization and real-time dense map reconstruction, and it recovers the metric scale even in GNSS-denied conditions. To achieve these advantages, Ultrawideband (UWB) measurements, monocular Visual Odometry (VO), and co-visibility observations are jointly employed to recover both relative positions and global UAV poses, thereby ensuring optimality at both local and global scales. In the proposed methodology, a two-stage optimization strategy is proposed to reduce optimization burden. Initially, relative Sim3 transformations among UAVs are swiftly estimated, with UWB measurements facilitating metric scale recovery in the absence of GNSS. Subsequently, a global pose optimization is performed to effectively mitigate cumulative drift. By integrating UWB, VO, and co-visibility data within this framework, both local geometric consistency and global pose accuracy are robustly maintained. Through comprehensive simulation and empirical real-world testing, we demonstrate that our system not only improves UAV positioning accuracy in challenging scenarios but also facilitates the high-quality, online integration of dense point clouds in large-scale areas. This research offers valuable contributions and practical techniques for precise, real-time map reconstruction using an autonomous UAV fleet, particularly in GNSS-denied environments.

Details

ICRA Conference 2024 Conference Paper

AutoFusion: Autonomous Visual Geolocation and Online Dense Reconstruction for UAV Cluster

Yizhu Zhang
Shuhui Bu
Yifei Dong 0008
Yu Zhang 0197
Kun Li
Lin Chen 0042

Real-time dense reconstruction using Unmanned Aerial Vehicle (UAV) is becoming increasingly popular in large-scale rescue and environmental monitoring tasks. However, due to the energy constraints of a single UAV, the efficiency can be greatly improved through the collaboration of multi-UAVs. Nevertheless, when faced with unknown environments or the loss of Global Navigation Satellite System (GNSS) signal, most multi-UAV SLAM systems can’t work, making it hard to construct a global consistent map. In this paper, we propose a real-time dense reconstruction system called AutoFusion for multiple UAVs, which robustly supports scenarios with lost global positioning and weak co-visibility. A method for Visual Geolocation and Matching Network (VGMN) is suggested by constructing a graph convolutional neural network as a feature extractor. It can acquire geographical location information solely through images. We also present a real-time dense reconstruction framework for multi-UAV with autonomous visual geolocation. UAV agents send images and relative positions to the ground server, which processes the data using VGMN for multi-agent geolocation optimization, including initialization, pose graph optimization, and map fusion. Extensive experiments demonstrate that our system can efficiently and stably construct large-scale dense maps in real-time with high accuracy and robustness.

Details

IROS Conference 2020 Conference Paper

DenseFusion: Large-Scale Online Dense Pointcloud and DSM Mapping for UAVs

Lin Chen 0042
Yong Zhao
Shibiao Xu
Shuhui Bu
Pengcheng Han
Gang Wan

With the rapidly developing unmanned aerial vehicles, the requirements of generating maps efficiently and quickly are increasing. To realize online mapping, we develop a real-time dense mapping framework named DenseFusion which can incrementally generates dense geo-referenced 3D point cloud, digital orthophoto map (DOM) and digital surface model (DSM) from sequential aerial images with optional GPS information. The proposed method works in real-time on standard CPUs even for processing high resolution images. Based on the advanced monocular SLAM, our system first estimates appropriate camera poses and extracts effective keyframes, and next constructs virtual stereo-pair from consecutive frame to generate pruned dense 3D point clouds; then a novel realtime DSM fusion method is proposed which can incrementally process dense point cloud. Finally, a high efficiency visualization system is developed to adopt dynamic levels of detail (LoD) method, which makes it render dense point cloud and DSM smoothly. The performance of the proposed method is evaluated through qualitative and quantitative experiments. The results indicate that compared to traditional structure from motion based approaches, the presented framework is able to output both large-scale high-quality DOM and DSM in real-time with low computational cost.

Details