Extrinsic self-calibration of multiple cameras with non-overlapping views in vehicles

Due to decreasing sensor prices and increasing processing performance, the use of multiple cameras in vehicles becomes an attractive possibility for environment perception. This contribution focuses on non-overlapping multi-camera configurations on a mobile platform and its purely vision-based self-calibration as well as its restrictions. The usage of corresponding features between the cameras is very difficult to realize and likely to fail due to different appearances in different views and motion-dependent time delays. Instead, the hand-eye calibration (HEC) technique based on visual odometry is considered to solve this problem by exploiting the cameras motions. For that purpose, this contribution presents an approach to continuously calibrate cameras by making use of the so-called motion adjustment (MA) and an IEKF. Visual odometry in driving vehicles often struggles in estimating the relative magnitudes of the translational motion, which is crucial for the HEC. So, MA simultaneously estimates the extrinsic parameters up to scale as well as the relative motion magnitudes. Furthermore, the estimation process is embedded into a global fusion framework to benefit from the redundant information resulting from multiple cameras in order to yield more robust results. This paper presents results with simulated and real data.