XGrid-Mapping: Explicit Implicit Hybrid Grid Submaps for Efficient Incremental Neural LiDAR Mapping

• URL: http://arxiv.org/abs/2512.20976v1
• Date: Wed, 24 Dec 2025 06:08:50 GMT
• Title: XGrid-Mapping: Explicit Implicit Hybrid Grid Submaps for Efficient Incremental Neural LiDAR Mapping
• Authors: Zeqing Song, Zhongmiao Yan, Junyuan Deng, Songpengcheng Xia, Xiang Mu, Jingyi Xu, Qi Wu, Ling Pei,
• Abstract summary: We propose a hybrid grid framework that exploits explicit and implicit representations for efficient neural LiDAR mapping.<n>By coupling the VDB structure with a submap-based organization, the framework reduces computational load.<n>Our approach delivers superior mapping quality while overcoming the efficiency limitations of voxel-guided methods.
• Score: 25.768483326085956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale incremental mapping is fundamental to the development of robust and reliable autonomous systems, as it underpins incremental environmental understanding with sequential inputs for navigation and decision-making. LiDAR is widely used for this purpose due to its accuracy and robustness. Recently, neural LiDAR mapping has shown impressive performance; however, most approaches rely on dense implicit representations and underutilize geometric structure, while existing voxel-guided methods struggle to achieve real-time performance. To address these challenges, we propose XGrid-Mapping, a hybrid grid framework that jointly exploits explicit and implicit representations for efficient neural LiDAR mapping. Specifically, the strategy combines a sparse grid, providing geometric priors and structural guidance, with an implicit dense grid that enriches scene representation. By coupling the VDB structure with a submap-based organization, the framework reduces computational load and enables efficient incremental mapping on a large scale. To mitigate discontinuities across submaps, we introduce a distillation-based overlap alignment strategy, in which preceding submaps supervise subsequent ones to ensure consistency in overlapping regions. To further enhance robustness and sampling efficiency, we incorporate a dynamic removal module. Extensive experiments show that our approach delivers superior mapping quality while overcoming the efficiency limitations of voxel-guided methods, thereby outperforming existing state-of-the-art mapping methods.

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