{'fraction_non_alphanumeric': 0.012853470437017995, 'fraction_numerical': 0.0, 'mean_word_length': 5.279569892473118, '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}}
Range and contour fused environment recognition for mobile robot

A sensor fusion scheme for mobile robot environment recognition that incorporates range data and contour data is proposed. An ultrasonic sensor provides coarse spatial description but guarantees open space (with no obstacle) within a sonic cone with relatively high belief. A laser structured light system provides a detailed contour description of the environment but is prone to light noise and is easily affected by surface reflectivity. We present a sensor fusion scheme that can compensate the disadvantages of both sensors. Line models from the laser structured light system play a key role in environment description. The overall fusion process is composed of two stages: noise elimination and belief updates. Dempster-Shafer's evidential reasoning is applied at each stage. Open space estimation from sonar range measurements brings elimination of noisy lines from the laser sensor. Comparing actual sonar data to the simulated sonar data enables data of two disparate sensors to be fused in the unified feature space. Experimental results demonstrate the effectiveness of the proposed method.