通过利用腿部里程计增强基于图的SLAM在GNSS受限环境中的性能
Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry
- LinxAI Tech（LinxAI科技）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出了一种基于因子图的架构，通过结合本体感觉腿部里程计和激光雷达-惯性里程计，有效减少了GNSS受限环境中视觉漂移，提高了SLAM的鲁棒性。
AI中文摘要：
在GNSS受限环境中，自主导航仍然是四足机器人面临的核心挑战，其中如激光雷达等外周传感器在几何稀疏或重复场景中容易产生高度漂移。我们提出了一种因子图架构，该架构通过并行运动学车道驱动由本体感觉腿部里程计提供的数据，并通过身份相对姿态约束与主要激光雷达-惯性车道连接，该约束采用选择性噪声模型。在Linxai D50四足平台上，该方法在两个总计超过一公里的户外环路中应用，将高度漂移从超过30米减少到不足30厘米，并在基线流程完全失败的场景中实现了收敛。这些结果表明，已经在机载系统中计算的本体感觉数据构成了轻量且有效的垂直锚点，用于GNSS受限环境下的SLAM。
英文摘要：
Autonomous navigation in GNSS-denied environments remains a core challenge for legged robots, where exteroceptive sensors such as LiDAR are prone to elevation drift in geometrically sparse or repetitive scenes. We present a factor graph architecture that augments the LIO-SAM framework with a parallel kinematic lane driven by proprioceptive leg odometry, coupled to the main LiDAR-inertial lane via an identity relative pose constraint with a selective noise model. Applied to a Linxai D50 quadruped platform across two outdoor loops totaling over one kilometer, our approach reduces elevation drift from over 30m to under 30cm and enables convergence in a scene where the baseline pipeline fails entirely. These results suggest that proprioceptive data, already computed onboard for gait control, constitutes a lightweight and effective vertical anchor for SLAM in GNSS-denied settings.
