{'fraction_non_alphanumeric': 0.012745098039215686, 'fraction_numerical': 0.000980392156862745, 'mean_word_length': 4.901734104046243, '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}}
Adaptive sensor-fusion of depth and color information for cognitive robotics

The presented work goes one step further than only combining data from different sensors. The corresponding points of an image and a 3D point cloud are determined through calibration. Color information is thereby assigned to every voxel in the overlapping area of a stereo camera system and a laser range finder. Then we analyze the image and search for the locations, which are especially susceptible to errors by both sensors. Depending on the ascertained situation, we try to correct or minimize errors. By analyzing and interpreting the images as well as removing errors we create an adaptive tool which improves multi-sensor fusion. This allows us to correct the fused data and to perfect the multi-modal sensor fusion or to predict the locations where the sensor information is vague or defective. The presented results demonstrate a clear improvement over standard procedures and show that other progress based on our work is possible.