activity
20192026
most citedAdaptive Tracking and Perching for Quadrotor in Dynamic Scenarios

64 citations · 88 across the 39 of their papers we have counts for

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Showing 2025Show all

11 papers · 1 filter

cs.RO2025

CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System

Zhehan Li, Zheng Wang, Jiadong Lu +6

Relative localization is essential for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions, or t…

cs.RO2025

Mr. Virgil: Learning Multi-robot Visual-range Relative Localization

Si Wang, Zhehan Li, Jiadong Lu +3

Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multi-agent relative localization. The challenging matching problem between rob…

cs.RO2025★ 1 cited

Neural Ranging Inertial Odometry

Si Wang, Bingqi Shen, Fei Wang +3

Ultra-wideband (UWB) has shown promising potential in GPS-denied localization thanks to its lightweight and drift-free characteristics, while the accuracy is limited in real scenar…

cs.CV2025

Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting

Juncheng Chen, Chao Xu, Yanjun Cao

Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view s…

cs.RO2025★ 6 cited

Global-State-Free Obstacle Avoidance for Quadrotor Control in Air-Ground Cooperation

Baozhe Zhang, Xinwei Chen, Qingcheng Chen +3

CoNi-MPC provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation…

cs.RO2025

Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles

Yuman Gao, Ruibin Zhang, Tiancheng Lai +3

Terrestrial-aerial bimodal vehicles, which integrate the high mobility of aerial robots with the long endurance of ground robots, offer significant potential for autonomous explora…