Heuristic vision based terrain recognition for lower limb exoskeletons.

For lower limb exoskeletons to be used for daily activities, it has to understand the diverse nature of its operating terrain. Computer vision offers an excellent avenue for terrain recognition for such applications. In this paper, we propose a heuristic vision-based approach for terrain recognition in lower limb exoskeletons, using line fitting features and active sensor fusion with exoskeleton encoders to enhance recognition performance. Our approach recognizes more terrain classes than traditional methods, including obstacles and gaps, enabling better terrain traversal. We estimate contextual features using sensor fusion to pre-process the point cloud and extract better discriminative terrain features for recognition. The recognized terrain and distance to it are used to modify gait strategy of the exoskeleton. The proposed approach has been validated with offline and online experiments. Validation experiments show successful integration with exoskeletons for traversal of different terrains.