Road Detection And Analysis, For The Radar Based Robot Navigation

For the research of outdoor intelligence robots, autonomous navigation in rough environment is an important factor. Because of the influence of complex conditions such as sunlight, texture and so on, some conventional analysis algorithms based on image data can hardly meet the demands. In this paper, we take the distribution of 3-D point cloud as the research object, design a new method of data analysis, that is, calibrate the radar data and image data to judge the obstacles and the transitable area. Also some relative experiments are carried on and the results will be analyzed.we can acquire the coordinates of point cloud by using laser radar to measure distance. The radar points will be put into one-to-one correspondence with pixels in image after calibrating the radar data and the camera data. In this way, we obtain the color information of radar points.Clustering the single line radar data by the nearest neighbor algorithm in various scenes including some complex ones. Compared the results of vision algorithm, we can find that laser radar can supply a gap of traditional methods. Gather multi-frames radar data and piece them together, we will obtain the panoramic scene in front. In order to cluster multi-frame data, color information is added as assistant, and then the transitable area will be picked-up.Occlusion detection is researched based on single line radar data. Recognise items which are shaded and also consistent with the linear distribution using weighted least squares linear fit. Improve the traditional beam curvature method, propose the condition of obstacle and choose the beam by calculating the goal function to avoid obstacles.In applications, this paper uses LMS291 radar scanner produced by SICK AG Inc., Germany to collect the data.