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Mobile-tracked robots are suitable for traversing rough terrain. However, standard exteroceptive localization methods (visual or laser SLAM) may be unreliable due to smoke, dust, fog, or insufficient lighting in harsh conditions of urban search and rescue missions. During extensive end-user evaluations in real-world conditions of such scenarios, we have observed that the accuracy of dead-reckoning localization suffers while traversing vertical obstacles. We propose to combine explicit modeling of robot kinematics and data-driven approach based on machine learning. The proposed method is experimentally verified indoors and outdoors traversing various obstacles. Indoors, a reference position has been recorded as well to assess the accuracy of our solution. The experimental dataset is released to the public to help the robotics community.
Kubelka et al. (Wed,) studied this question.