外观
论文arXiv 2610.2119913 天前
LIN-SLAM：用于实时机载导航的轻量级 LiDAR--惯性神经 SLAM
LIN-SLAM融合几何LiDAR惯性估计与神经隐式建图，在多数据集和Go2真机上验证实时机载SLAM。
宇树 Go2原文链接：LIN-SLAM: Lightweight LiDAR--Inertial Neural SLAM for Real-Time Onboard Navigation（arXiv 2610.21199 · 发布日期：2026-10-15）
LIN-SLAM: Lightweight LiDAR--Inertial Neural SLAM for Real-Time Onboard Navigation
LIN-SLAM融合几何LiDAR惯性估计与神经隐式建图，在多数据集和Go2真机上验证实时机载SLAM。
核心要点
- 几何LiDAR惯性SLAM高效但缺少连续地图与全局细化，神经隐式方法计算开销大。
- 方法耦合高频几何LiDAR惯性估计、神经隐式跟踪、持续建图和全局细化。
- 在三数据集十二个序列上，总体平均ATE最低，并较现有神经隐式SLAM显著降低运行时。
- 在Go2平台1.47公里室内走廊实时运行，端点闭合误差0.141米；代码接收后发布。
验证范围与局限：验证包括三个数据集的十二个序列，以及一台搭载Jetson Orin NX和16线LiDAR的Unitree Go2室内轨迹。
英文摘要 (Abstract)
Geometric light detection and ranging (LiDAR)--inertial simultaneous localization and mapping (SLAM) provides efficient state estimation but lacks the continuous map representation and global refinement capabilities of neural implicit SLAM, while neural implicit methods remain computationally expensive. We propose Lightweight LiDAR--Inertial Neural SLAM (LIN-SLAM), a hybrid method that couples high-rate geometric LiDAR--inertial estimation with neural implicit tracking, continual mapping, and global refinement for accurate real-time onboard SLAM. Experiments on twelve representative sequences from three datasets spanning 32-, 64-, and 128-channel LiDAR configurations show that LIN-SLAM achieves the lowest overall mean absolute trajectory error (ATE) among the evaluated state-of-the-art geometric and neural implicit methods, while substantially reducing runtime compared with existing neural implicit SLAM methods. On a Unitree Go2 equipped with an NVIDIA Jetson Orin NX and a 16-channel LiDAR, LIN-SLAM sustains real-time localization and neural mapping over a 1.47-km indoor trajectory through challenging repetitive corridor environments, with an endpoint closure error of only 0.141~m. To the best of our knowledge, LIN-SLAM is the first LiDAR--inertial SLAM system with a neural implicit backend to achieve real-time online operation on a mobile robotic platform while maintaining state-of-the-art localization accuracy. The code will be released upon acceptance.
作者：Shiqi Che, Yijie Liu, Shuo Wang, Wenxin Liu
来源
本页事实对应的公开页面，以原文为准。