用腿部自感知数据降低机器人在无卫星信号环境下的定位误差
Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry
- 用腿部本体感知数据构建并行运动链，与激光惯性系统协同
- 在1公里户外路径上将俯仰漂移从30米降至30厘米以下
- 适合需要高精度垂直定位的四足机器人导航场景
在无卫星信号环境下，四足机器人自主导航仍面临核心挑战。当外部传感器（如激光雷达）在几何稀疏或重复场景中易产生高度漂移时，本文提出一种因子图架构，将基于本体感知的腿部里程计作为并行运动链，与主激光-惯性通道通过带有选择性噪声模型的身份相对位姿约束耦合。在林犀D50四足平台上对两条总长超1公里的户外环路进行测试，所提方法将高度漂移从超过30米降至不足30厘米，并使原本完全失效的基准流程实现收敛。结果表明，用于步态控制的本体感知数据可作为轻量高效的高度锚点，在无卫星信号环境中显著提升SLAM性能。
原文摘要 · Abstract (English)
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.
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