Calibration of Perception Sensors
Examensarbete för masterexamen
Production engineering (MPPEN), MSc
New regulations mandating specific sensors coupled with the trend towards manufacturing trucks with more self-driving capabilities has led to an increase in both the number and types of perception sensors on each new model of trucks. With each new model, the need for proper calibration becomes paramount and demands an investigation into what sensor technologies will be required to reach a higher level of autonomy. Further, proper calibration is paramount to ensure the accuracy concerning the physical location of the objects in the truck’s surrounding and allowing the truck to safely navigate. This thesis therefore explores perception sensors in the context of manufacturing trucks. The research investigates the currently employed calibration methods used for trucks and various calibration methods for different sensors. Both quantitative and qualitative data was gathered from an in depth AS-IS analysis and a literature study on both calibration methods and the sensor technologies themselves. This is done in order to form the basis for recommending concepts of future calibration stations. The results presented in this thesis include three variations of a high-volume factory calibration station and one variation for a low-volume factory. The four different concepts all utilize the same calibration methods; however, the pieces of equipment and layouts differ among them. The concepts are capable of handling camera, LiDAR, camera stitching and Radar, with different process times. The conclusion is therefore that both low and high-volume factories should share the same calibration methods enabling interoperability and scalability. The difference between them is the amount of equipment and investment needed. For high-volume factories where time is critical and multiple cobots are employed and utilizes parallel calibration. In contrast, low-volume factories where time is not as critical fewer cobots are employed and utilizes sequential calibration. This in turn entails that high-volume concepts need greater investment and are more complex. Nonetheless both concepts can benefit from shared knowledge and resources. The proposed concepts serve as a foundation for future calibration stations where trucks of a higher degree of autonomy can be produced.
: Calibration, Sensor, Perception sensors, Radar, LiDAR, Camera, Camera Stitching, Point Cloud