Supervised Articulation Angles Estimation for Multi-Articulated Vehicles Based on Panoramic Camera System

Articulation angle plays a significant role in determining the motion of a complex dynamic system such as a multi-articulated vehicle. By engineering practice, articulation angles are measured using mechanical angle sensors that are delicate to physical damage. To overcome this problem, this study proposed a supervised articulation angle estimation method based on the panoramic camera system of multi-articulated vehicles. By constructing neural network that takes images of surrounding environment captured by spatially adjacent cameras as input, and takes temporal dependency as well as data imbalanced distribution into consideration, we show that the proposed vision-only method could make accurate estimations either on collected dataset or field experiment. Results of our experiments verified the validity and feasibility of the proposed method in playing as an alternative to mechanical angle sensors without bringing additional hardware setting expenses.