Environment awareness is crucial for enhancing walking safety and stability of amputee wearing powered prosthesis when crossing uneven terrains such as stairs and obstacles. However, existing environmental perception systems for prosthesis only provide terrain types and corresponding parameters, which fails to prevent potential collisions when crossing uneven terrains and may lead to falls and other severe consequences. In this paper, a visual-inertial motion estimation approach is proposed for prosthesis to perceive its movement and the changes of spatial relationship between the prosthesis and uneven terrain when traversing them. To achieve this, we estimate the knee motion by utilizing a depth camera to perceive the environment and align feature points extracted from stairs and obstacles. Subsequently, an error-state Kalman filter is incorporated to fuse the inertial data into visual estimations to reduce the feature extraction error and obtain a more robust estimation. The motion of prosthetic joint and toe are derived using the prosthesis model parameters. Experiment conducted on our collected dataset and stair walking trials with a powered prosthesis shows that the proposed method can accurately tracking the motion of the human leg and prosthesis with an average root-mean-square error of toe trajectory less than 5 cm. The proposed method is expected to enable the environmental adaptive control for prosthesis, thereby enhancing amputee's safety and mobility in uneven terrains.

翻译：环境感知对于提升穿戴动力假肢的截肢者在跨越楼梯、障碍物等不平坦地形时的行走安全性和稳定性至关重要。然而，现有假肢环境感知系统仅能提供地形类型及对应参数，无法在跨越不平坦地形时预防潜在碰撞，可能导致跌倒等严重后果。本文提出一种基于视觉-惯性的假肢运动估计方法，使假肢能够感知自身运动及与不平坦地形间空间关系的变化。具体而言，我们通过深度相机感知环境，提取楼梯和障碍物上的特征点进行对齐，从而估计膝关节运动。随后引入误差状态卡尔曼滤波器，将惯性数据与视觉估计融合，以降低特征提取误差并获得更稳健的估计结果。利用假肢模型参数推导出假肢关节和足尖的运动。在自建数据集及动力假肢楼梯行走实验中的验证表明，该方法能够精确追踪人体腿部与假肢的运动，足尖轨迹平均均方根误差小于5厘米。该方法有望实现假肢的环境自适应控制，从而提升截肢者在不平坦地形中的安全性和行动能力。