Roll angle estimation for motorcycles: Comparing video and inertial sensor approaches

Abstract

Advanced Rider Assistance Systems (ARAS) for powered two-wheelers improve driving behaviour and safety. Further developments of intelligent vehicles will also include video-based systems, which are successfully deployed in cars. Porting such modules to motorcycles, the camera pose has to be taken into account, as e. g. large roll angles produce significant variations in the recorded images. Therefore, roll angle estimation is an important task for the development of various kinds of ARAS. This study introduces alternative approaches based on inertial measurement units (IMU) as well as video only. The latter learns orientation distributions of image gradients that code the current roll angle. Until now only preliminary results on synthetic data have been published. Here, an evaluation on real video data will be presented along with three valuable improvements and an extensive parameter optimisation using the Covariance Matrix Adaptation Evolution Strategy. For comparison of the very dissimilar approaches a test vehicle is equipped with IMU, camera and a highly accurate reference sensor. The results state high performance of about 2 degrees error for the improved vision method and, therefore proofs the proposed concept on real-world data. The IMU-based Kalman filter estimation performed on par. As a naive result averaging of both estimates already increased performance an elaborate fusion of the proposed methods is expected to yield further improvements.