LXD-SLAM：基于LiDAR+X的密集SLAM，支持∑_{i=0}^{5}C_5^i种可配置传感器组合
LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations
AI总结：
提出LXD-SLAM，一种以3D LiDAR为中心的多传感器融合框架，支持32种传感器组合，通过统一的迭代误差状态卡尔曼滤波和混合位姿图实现高保真、全局一致的密集建图。
摘要：
同步定位与地图构建（SLAM）对于自主系统至关重要，但由于几何退化与传感器漂移，在复杂环境中实现可靠的全局一致位姿估计与密集建图仍具挑战。多传感器融合可解决这些问题，但现有系统缺乏适应不同平台的模块化，且依赖数学上不一致的融合或次优的地图表示。为克服这些局限，我们提出LXD-SLAM（LiDAR+X密集SLAM），一种高度通用且统一的多传感器融合框架。该系统以3D LiDAR为核心，支持LiDAR、相机、IMU、轮式编码器和GNSS的即插即用集成，提供多达32种不同的传感器组合。我们采用数学统一的迭代误差状态卡尔曼滤波器，结合自适应分层预测策略和最小化点-网格距离与视觉重投影误差的更新步骤。为此，环境使用连续多层高斯过程（GP）子网格建模，从而能够高效地恢复视觉特征的射线-网格深度。为保持全局一致性，我们引入从GP子网格导出的扩展扫描上下文（ESC）描述符，以及双向PnP优化，用于混合位姿图中的鲁棒多模态闭环。在公开数据集和真实世界实验上的广泛评估表明，LXD-SLAM在各种配置下匹配或超越最先进的专用里程计方案，同时实时生成高保真、全局一致的密集网格。相关代码和数据将在发表后于https://this URL提供。
英文摘要：
Simultaneous Localization and Mapping (SLAM) is essential for autonomous systems, yet achieving reliable, globally consistent pose estimation and dense mapping in complex environments remains challenging due to geometric degeneracy and sensor drift. While multi-sensor fusion addresses these issues, existing systems often lack the modularity to adapt to diverse platforms and rely on mathematically inconsistent fusion or suboptimal map representations. To address these limitations, we propose LXD-SLAM (LiDAR+X Dense SLAM), a highly versatile and unified multi-sensor fusion framework. Centered around 3D LiDAR, our system allows for the plug-and-play integration of LiDAR, Camera, IMU, Wheel Encoder, and GNSS, supporting up to 32 distinct sensor combinations. We employ a mathematically unified Iterative Error-Sate Kalman Filter with an adaptive hierarchical prediction strategy and an update step that minimizes point-to-mesh distances and visual reprojection errors. To support this, the environment is modeled using continuous multi-layered Gaussian Process (GP) sub-meshes, which enables efficient ray-to-mesh depth recovery for visual features. For global consistency, we introduce an Extended Scan Context (ESC) descriptor derived from the GP sub-meshes alongside a Bidirectional PnP optimization for robust multi-modal loop closure within a hybrid pose graph. Extensive evaluations on public datasets and real-world experiments demonstrate that LXD-SLAM matches or exceeds state-of-the-art specialized odometry solutions across various configurations while generating high-fidelity, globally consistent dense meshes in real-time. The relevant codes and data will be made available at https://github.com/peterWon/LXD-SLAM upon publication.
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