  Multi-modal object detection in autonomous driving has achieved great
breakthroughs due to the usage of fusing complementary information from
different sensors. The calibration in fusion between sensors such as LiDAR and
camera is always supposed to be precise in previous work. However, in reality,
calibration matrices are fixed when the vehicles leave the factory, but
vibration, bumps, and data lags may cause calibration bias. As the research on
the calibration influence on fusion detection performance is relatively few,
flexible calibration dependency multi-sensor detection method has always been
attractive. In this paper, we conducted experiments on SOTA detection method
EPNet++ and proved slight bias on calibration can reduce the performance
seriously. We also proposed a re-calibration model based on semantic
segmentation which can be combined with a detection algorithm to improve the
performance and robustness of multi-modal calibration bias.
