6 papers
UAV-MapFusion: RTK-Aligned Uncertainty-Aware Coarse-to-Fine Multi-Session UAV Mapping
Feng Pan, Chunran Zheng, Bing Xue +4
Large-scale point cloud maps are essential for robotics and spatial intelligence tasks. UAVs provide an efficient means for large-scale map acquisition; however, due to limited fli…
MIL-LC: A Robust Magnetometer-Inertial-LiDAR Fusion Multimodal Localization Framework
Qiyang Lyu, Zhenyu Wu, Wei Wang +2
Localization in challenging environments, such as GNSS-denied, geometrically repetitive, or textureless scenes commonly found in offices, hotels, and underground parking facilities…
RoSLAC: Robust Simultaneous Localization and Calibration of Multiple Magnetometers
Qiyang Lyu, Zhenyu Wu, Wei Wang +2
Localization of autonomous mobile robots (AMRs) in enclosed or semi-enclosed environments such as offices, hotels, hospitals, indoor parking facilities, and underground spaces wher…
UniLGL: Learning Uniform Place Recognition for FOV-limited/Panoramic LiDAR Global Localization
Hongming Shen, Xun Chen, Yulin Hui +5
Existing LGL methods typically consider only partial information (e.g., geometric features) from LiDAR observations or are designed for homogeneous LiDAR sensors, overlooking the u…
L2M-Calib: One-key Calibration Method for LiDAR and Multiple Magnetic Sensors
Qiyang Lyu, Wei Wang, Zhenyu Wu +3
Multimodal sensor fusion enables robust environmental perception by leveraging complementary information from heterogeneous sensing modalities. However, accurate calibration is a c…
CTE-MLO: Continuous-time and Efficient Multi-LiDAR Odometry with Localizability-aware Point Cloud Sampling
Hongming Shen, Zhenyu Wu, Yulin Hui +6
In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. Considering single…