ScholarWorks@중앙대학교

초록

Foot placement position (FP) and step height (SH) are needed to control walking-assistive systems on uneven terrain. This study proposes a novel model that predicts FP and SH before a user takes a step. The model uses a stereo vision system mounted on the upper body and adapts to various terrains by incorporating foot motions and terrain object information. First, FP was predicted by visually tracking foot positions and was corrected based on the types and locations of objects on the ground. Then, SH was estimated using depth maps captured by an RGB-D stereo camera. To predict SH, several RGB-D frames were considered with homography, feature matching, and image transformation. The results show that the heatmap trajectory improved FP prediction on the flat-walking dataset, reducing the root mean square error of FP from 20.89 to 17.70 cm. Furthermore, incorporating object preference significantly improved FP prediction, resulting in an accuracy improvement from 52.57% to 78.01% in identifying the object a user stepped on. The mean absolute error of SH was calculated to be 7.65 cm in scenes containing rocks and puddles. The proposed model can enhance the control of walking-assistive systems in complex environments.

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