Spatio-Temporal Ultrasonic Dataset: Learning Driving from Spatial and Temporal Ultrasonic Cues

Recent works have proved that combining spatial and temporal visual cues can significantly improve the performance of various vision-based robotic systems. However, for the ultrasonic sensors used in most robotic tasks (e.g. collision avoidance, localization and navigation), there is a lack of benchmark ultrasonic datasets that consist of spatial and temporal data to verify the usability of spatial and temporal ultrasonic cues. In this paper, we are the first to propose a Spatio-Temporal Ultrasonic Dataset (STUD), which aims to develop the ability of ultrasonic sensors by mining spatial and temporal information from multiple ultrasonic measurements. In particular, we first propose a novel Spatio-Temporal (ST) ultrasonic data gathering scheme, in which an innovatory data instance is designed. Besides, part of the data in the STUD is collected in a robot simulator, in which a well-designed corridor map is utilized to increase the data diversity. Then a selection algorithm is proposed to find a proper length of data sequences to obtain the best description of the navigation environments. Finally, we present an end-to-end learning benchmark model that learns driving policies by extracting spatial and temporal ultrasonic cues from the STUD. With the help of our STUD and this benchmark model, more powerful deep neural networks can be trained for addressing the tasks of indoor navigation or motion planning of mobile robots, which is unachievable by using the existing ultrasonic datasets. Comparison experiments verified the effectiveness of spatial and temporal ultrasonic cues for the robot driving policy learning.