Optimization Design of Joint Calibration for Roadside Multi-sensor Fusion

Abstract:

In the roadside perception， the traffic scene is complex and changeable， and a single type of sensors cannot meet the requirement of all-weather and all-day work. Therefore， the multi-sensor fusion has gradually become a development trend， which is on the premise that the joint calibration among sensors should be achieved. In this paper， by placing markers in the common view area of lidar and camera， the point cloud coordinate and corresponding pixel coordinate of feature points are obtained， and external parameters between lidar and camera are obtained by EPnP （Efficient Perspective-n-Points） algorithm. The point cloud of the lidar on the road surface is up-sampled， and corresponding pixel coordinates are generated according to the matrix of the joint calibration. The model is trained to fit the point cloud and pixel by using the algorithm of decision tree regression and random forests regression， and then the pixel coordinate is converted to WGS84 coordinate according to the results of lidar calibration. This method does not need the integration of lidar and camera. The effectiveness of the proposed joint calibration scheme is verified by the experiment.