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20242026
most citedHeterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios

1 citations · 3 across the 5 of their papers we have counts for

collaborators

7 papers

cs.RO2026

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…

cs.RO2025★ 1 cited

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…

cs.RO2024

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…

cs.RO2024

GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting

Yusen Xie, Zhenmin Huang, Jin Wu +1

In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing m…

cs.RO2024★ 1 cited

Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios

Zhiqiang Chen, Yuhua Qi, Dapeng Feng +6

The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometri…

cs.RO2024

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…

cs.RO2024★ 1 cited

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…