1 citations · 2 across the 5 of their papers we have counts for
8 papers
VeriCalib: Verifiable Target-Based LiDAR-Camera Calibration
Xiangcheng Hu, Xieyuanli Chen, Feiyi Chen +8
Target-based extrinsic calibration estimates the rigid transform between sensors from a few observations of a known target, and its result is commonly assessed by the root-mean-squ…
Graph3R: Unifying View Graph Initialization with Feed-Forward Reconstruction
Jingwen Yu, Hengli Wang, Jianhao Jiao +3
Feed-forward reconstruction recovers camera poses and dense geometry from image sets whose views are assumed to come from a single scene. In practice, uncurated image collections o…
DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration
Xiangcheng Hu, Xieyuanli Chen, Mingkai Jia +3
LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), registration becomes ill-conditioned…
ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments
Jingwen Yu, Jiayi Yang, Anjun Hu +5
Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical keyframes, achieving drift correction an…
MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework
Xiangcheng Hu, Jin Wu, Mingkai Jia +6
Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluat…
MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System
Xiangcheng Hu, Jin Wu, Jianhao Jiao +4
Large-scale multi-session LiDAR mapping is essential for a wide range of applications, including surveying, autonomous driving, crowdsourced mapping, and multi-agent navigation. Ho…
GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection
Jingwen Yu, Hanjing Ye, Jianhao Jiao +2
Visual loop closure detection is an important module in visual simultaneous localization and mapping (SLAM), which associates current camera observation with previously visited pla…
MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection
Xiangcheng Hu, Jin Wu, Jianhao Jiao +2
Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This pape…