{'fraction_non_alphanumeric': 0.01674641148325359, 'fraction_numerical': 0.0007974481658692185, 'mean_word_length': 5.338383838383838, '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}}
Motion-based calibration of multimodal sensor arrays

This paper formulates a new pipeline for automated extrinsic calibration of multi-sensor mobile platforms. The new method can operate on any combination of cameras, navigation sensors and 3D lidars. Current methods for extrinsic calibration are either based on special markers and/or chequerboards, or they require a precise parameters initialisation for the calibration to converge. These two limitations prevent them from being fully automatic. The method presented in this paper removes these restrictions. By combining information extracted from both, platform's motion estimates and external observations, our approach eliminates the need for special markers and also removes the need for manual initialisation. A third advantage is that the motion-based automatic initialisation does not require overlapping field of view between sensors. The paper also provides a method to estimate the accuracy of the resulting calibration. We illustrate the generalisation of our approach and validate its performance by showing results with two contrasting datasets. The first dataset was collected in a city with a car platform, and the second one was collected in a tree-crop farm with a Segway platform.