3 citations · 7 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
MCN-SLAM: Multi-Agent Collaborative Neural SLAM with Hybrid Implicit Neural Scene Representation
Tianchen Deng, Guole Shen, Xun Chen +9
Neural implicit scene representations have recently shown promising results in dense visual SLAM. However, existing implicit SLAM algorithms are constrained to single-agent scenari…
IDF-MFL: Infrastructure-free and Drift-free Magnetic Field Localization for Mobile Robot
Hongming Shen, Zhenyu Wu, Wei Wang +3
In recent years, infrastructure-based localization methods have achieved significant progress thanks to their reliable and drift-free localization capability. However, the pre-inst…
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…
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
Jun Zhang, Huayang Zhuge, Yiyao Liu +12
Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke an…
Semantic Reinforced Attention Learning for Visual Place Recognition
Guohao Peng, Yufeng Yue, Jun Zhang +3
Large-scale visual place recognition (VPR) is inherently challenging because not all visual cues in the image are beneficial to the task. In order to highlight the task-relevant vi…
Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization
Zhenyu Wu, Mingxing Wen, Guohao Peng +2
Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environment…