activity
20242026
collaborators

9 papers

cs.CV2026

LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization

Wen Li, Shangshu Yu, Dunqiang Liu +5

Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical…

cs.RO2026

Empowering a Single-Frequency GNSS Receiver to Achieve High-Precision Positioning with Relative Observations

Xingpeng Wang, Ziwen Qu, Juncheng Chen +8

Global Navigation Satellite System (GNSS) navigation is widely used to provide absolute, outdoor positioning in field robotics. Advances in Real-Time Kinematic (RTK) technology can…

cs.LG2026

Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods

Zhengzhu Liu, Zeming Gao, Haoyuan Qin +7

Multi-Agent Reinforcement Learning (MARL) value factorization methods can suffer from a loss of plasticity, gradually failing to adapt when transferring to new task instances. We t…

cs.CV2026

MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations

Yuhua Luo, Junsheng Zhang, Mengyin Liu +7

Human motion follows a temporal hierarchical structure, transitioning from low-frequency global trajectories to high-frequency details. Inspired by the success of multi-level autor…

cs.RO2026

Explore From Sketch: Accelerating UAV Exploration in Large-scale Environments with Prior Maps

Tiancheng Lai, Yuman Gao, Xiangyu Li +8

Autonomous exploration with UAVs in large-scale, topologically complex environments often suffers from low efficiency due to suboptimal scheduling and detours. Prior maps (e.g., co…

cs.CV2026

LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization

Jianshi Wu, Minghang Zhu, Dunqiang Liu +5

LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-based regression methods offer…