研究目的
To accurately estimate extrinsic calibration parameters between stereo vision camera and 2D laser range finder (LRF) based on 3D reconstruction of monochromatic calibration board and geometric co-planarity constraints between the views from these two sensors.
研究成果
The proposed method for automatic calibration of stereo vision camera and 2D LRF using a monochromatic calibration board yields accurate and precise calibration results. It reduces the uncertainties in the LRF data due to range-reflectivity-bias and eliminates the need for manual intervention in extracting plane-line correspondences.
研究不足
The study focuses on the calibration between a stereo vision camera and a 2D LRF using a monochromatic calibration board. The limitations include the dependency on the quality of the calibration board and the environmental conditions during data acquisition.
1:Experimental Design and Method Selection:
The study uses a monochromatic calibration board for automatic extraction of plane-line correspondences between camera and LRF. It involves selecting optimal threshold values for laser scan dissection to extract line features from LRF data.
2:Sample Selection and Data Sources:
The experiment involves placing a calibration board at different positions and orientations viewed simultaneously by both the sensors. For every pose, vertices of the calibration board are extracted from the left and right images.
3:List of Experimental Equipment and Materials:
The setup includes a stereoscopic system (Bumblebee XB2) and a 2D LRF (Hokuyo UTM-30LX-EW) mounted on the top of the vehicle.
4:Experimental Procedures and Operational Workflow:
The process includes automatic extraction of calibration board plane from the image data, automatic line feature extraction from the LRF data, and estimation of extrinsic calibration parameters by registering the selected line features from laser scan with the estimated calibration board plane from the stereo vision camera images.
5:Data Analysis Methods:
The study uses Levenberg-Marquardt optimisation method for solving the constraints and minimising the errors.
独家科研数据包,助您复现前沿成果,加速创新突破
获取完整内容