A data-driven and application-aware approach to sensory system calibration in an autonomous vehicle

The existing, universal approaches to sensory system calibration do not consider target application during calibration. We propose a new approach to calibration that uses the target application processing pipeline to measure the calibration quality. This approach is exemplified and tested in the calibration of an onboard vision system used to localize a city bus to an electric charging station. Our calibration procedure determines the parameters applying optimization and automatic features detection from a deep learning-based vision processing pipeline. The parameters are calibrated to produce localization estimates matching the ground truth pose measurements. We measure the calibration’s performance in a target application on over 10000 poses recorded from a real city bus over several days. The automatic calibration procedure results in numerous optimization constraints, which can be used to calibrate more parameters and reduce manual labor. The proposed approach reduces the localization errors by almost 50% in the presented target application. •A new calibration that focuses on the system’s performance in the application.•A new data-driven calibration procedure utilizing all available measurements.•A calibration process that supports black-box modules based on deep learning.•A calibration procedure that requires no calibration-specific data registration.