LACI: Low-effort Automatic Calibration of Infrastructure Sensors

Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well as a widely overlapping field of view (FOV), within this work, a cooperative intelligent vehicle is used as callibration target. The vehicle is detected in the sensor frame and then matched with the information received from the cooperative awareness messages send by the coperative intelligent vehicle. The presented algorithm is fully automated as well as sensor-independent, relying only on a very common set of assumptions. Due to the direct registration on the world frame, no overlapping FOV is necessary. The algorithm is evaluated through experiment for four laserscanners as well as one pair of stereo cameras showing a repetition error within the measurement uncertainty of the sensors. A plausibility check rules out systematic errors that might not have been covered by evaluating the repetition error.

[1]  Michael Bosse,et al.  Line-based extrinsic calibration of range and image sensors , 2013, 2013 IEEE International Conference on Robotics and Automation.

[2]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[3]  Esra Ataer Cansizoglu,et al.  Calibration of Non-overlapping Cameras Using an External SLAM System , 2014, 2014 2nd International Conference on 3D Vision.

[4]  Klaus C. J. Dietmayer,et al.  Simulation and calibration of infrastructure based laser scanner networks at intersections , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[5]  Yohan Dupuis,et al.  A Survey of Vision-Based Traffic Monitoring of Road Intersections , 2016, IEEE Transactions on Intelligent Transportation Systems.

[6]  Kai Hormann,et al.  The point in polygon problem for arbitrary polygons , 2001, Comput. Geom..

[7]  Tim J. Ellis,et al.  Calibration and object correspondence in camera networks with widely separated overlapping views , 2015, IET Comput. Vis..

[8]  Vasseur Pascal,et al.  Rotation and translation estimation for a wide baseline fisheye-stereo at crossroads based on traffic flow analysis , 2016 .

[9]  Martin Lauer,et al.  Automatic Calibration of Multiple Cameras and Depth Sensors with a Spherical Target , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Marcelo H. Ang,et al.  Efficient L-shape fitting of laser scanner data for vehicle pose estimation , 2015, 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[11]  Klaus C. J. Dietmayer,et al.  Feature-based mapping and self-localization for road vehicles using a single grayscale camera , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[12]  Chen Zhu,et al.  Robust Plane-Based Calibration of Multiple Non-Overlapping Cameras , 2016, 2016 Fourth International Conference on 3D Vision (3DV).