Research On Local Environment Mapping For Autonomous Vehicle

Environment detection based on multi-sensor is the pre-condition that autonomous vehicle can drive precisely and safely. By far, there is no one single sensor can provide complete and reliable data of the environment, so data fusion is one of the key technologies for local environment mapping.This thesis is about research on the mapping strategy based on multi-sensor. It uses data fusion to establish a reliable local map. It's a hot research on establishing map under un-constructive environment, especially complicated environment in the city, and it will improve the intelligent level of autonomous vehicle.The main contributions and works are described as follows1. This thesis proposes a combination algorithm based on ABD (adaptive breakpoint detector) algorithm and linear threshold segmentation algorithm after some comparisons on several segmentation algorithms. After segmentation, it is easily to detect the noise point based on clusters, then shows the constructive environment using line extraction algorithm.2. This thesis proposes a new SCSD (Sharp Change Sequence Detector) algorithm to detect abnormal data based on tremendous difference between frames by analyzing the sequence of LRF data when the points of current frame is nearly maximum.3. Matlab camera calibration toolbox and OpenCV camera calibration method are used to get camera parameters, then un-distorts the image by distortion coefficient and uses IPM (Inverse Perspective Mapping) to convert the ROI (Region of Interest) pixel to physical distance precisely.4. Using rotation matrix and translation matrix of two Cartesian coordinate planes to unify the LRF and camera coordinates, and establishes a local environment map. It takes advantages from two sensors.