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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…
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…
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…
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…
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…