用有限视场深度相机实现外骨骼精准里程计
Leg Exoskeleton Odometry using a Limited FOV Depth Sensor
- 融合自身体感数据与点云，构建抗漂移的高精度地形图
- 实验显示误差比纯体感方法降低40%，优于传统点云地图方法
- 适合外骨骼等穿戴设备在复杂地形中的定位与导航
为使下肢外骨骼在真实环境中有效运行，必须能够感知并理解周围地形。然而，与其它腿式机器人不同，外骨骼受限于人体用户的存在，导致深度传感器安装位置受限，视野狭窄且运动剧烈，使得里程计尤为困难。为此，我们提出一种新型里程计算法，将外骨骼的本体感知数据与深度相机获取的点云数据融合，即使在上述限制下也能生成准确的高程图。该方法基于扩展卡尔曼滤波（EKF）融合运动学和惯性测量数据，并采用定制化的迭代最近点（ICP）算法将新点云与高程图对齐。在真实外骨骼上的实验验证表明，相比纯本体感知基线，本方法显著减少漂移并提升高程图质量，同时优于传统的基于点云地图的方法。
原文摘要 · Abstract (English)
For leg exoskeletons to operate effectively in real-world environments, they must be able to perceive and understand the terrain around them. However, unlike other legged robots, exoskeletons face specific constraints on where depth sensors can be mounted due to the presence of a human user. These constraints lead to a limited Field Of View (FOV) and greater sensor motion, making odometry particularly challenging. To address this, we propose a novel odometry algorithm that integrates proprioceptive data from the exoskeleton with point clouds from a depth camera to produce accurate elevation maps despite these limitations. Our method builds on an extended Kalman filter (EKF) to fuse kinematic and inertial measurements, while incorporating a tailored iterative closest point (ICP) algorithm to register new point clouds with the elevation map. Experimental validation with a leg exoskeleton demonstrates that our approach reduces drift and enhances the quality of elevation maps compared to a purely proprioceptive baseline, while also outperforming a more traditional point cloud map-based variant.
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