{'fraction_non_alphanumeric': 0.020274689339437543, 'fraction_numerical': 0.0006540222367560497, 'mean_word_length': 5.538461538461538, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Visual Environment perception for obstacle detection and crossing of lower-limb exoskeletons

Lower limb exoskeletons offer support for patients suffering from mobility disorders due to injury, stroke, etc. But these devices are not used in day-to-day life and environments due to their limited human-computer interface to perceive and handle different terrains and tasks. In this paper, we introduce a simple vision-based environment perception pipeline for lower- limb exoskeletons for obstacle crossing tasks. The proposed pipeline consists of three stages, namely, ground plane and obstacle detection, estimating obstacle location and dimensions, and obstacle tracking. To reduce noisy artifacts and reliably detect obstacles, we propose a similarity metric based on color, gradient orientation, and 2D surface normal. Depth map of the detected obstacle region is utilized for estimating the obstacle location and dimensions. Also, we consider two obstacle tracking modes for obstacle crossing, visual tracking using a RGB-D camera and positional tracking using a SLAM camera. The proposed vision-based perception pipeline is integrated with an exoskeleton, where we propose a control scheme that can vary step length adaptively to successfully cross detected obstacles. We conduct offline and online experiments to validate the proposed perception pipeline and provide insights on the same. Our experiments show that the proposed pipeline allows exoskeletons to understand their environment and successfully cross obstacles.