Road drivable area detection method based on fusion of monocular vision and lidar

Abstract

The invention discloses a monocular vision and laser radar fusion-based road travelable region detection method and belongs to the intelligent transportation field. Existing unmanned vehicle road detection methods are mainly based on methods such as monocular vision, stereo vision, laser sensor and multi-sensor fusion methods, have defects of low robustness to illumination, complex three-dimensional matching, laser sparseness, low overall fusion efficiency and the like. Although some supervised methods have achieved better accuracy, the training processes of the supervised methods are complex, and the generalization effects of the supervised methods are poor. According to the monocular vision and laser radar fusion-based road travelable region detection method provided by the present invention, ultra-pixel and point cloud data fusion is adopted; on the basis of features, road regions can be obtained through machine self learning; and the features are fused through the Bayesian frame, so that road information is obtained, and a final region can be obtained. With the method adopted, strong hypothesis information and complex training processes are not required. The monocular vision and laser radar fusion-based road travelable region detection method has the advantages of excellent generalization performance, high robustness, fast speed and high precision, and can be popularized and used more easily in practical application.

Description

technical field [0001] The invention belongs to method research in the field of intelligent transportation, and relates to a road drivable area detection method based on the fusion of monocular vision and laser radar. Background technique [0002] In recent years, road detection has been an important part of research in the field of driverless driving. Currently widely used road detection methods are: monocular vision method, stereo vision method, lidar method and fusion-based method. Among them, the monocular vision method only considers the visual information of the scene, which is easily affected by lighting conditions and weather conditions; the stereo vision method consumes a lot of time in 3D reconstruction and is not suitable for practical applications; the lidar method has point cloud data Sparse disadvantage. The road detection method based on the fusion of pixel information and depth information not only makes full use of the texture, color and other information ...

AI Technical Summary

Problems solved by technology

Among them, the monocular vision method only considers the visual information of the scene, which is easily affected by lighting conditions and weather conditions; the stereo vision method consumes a lot of time in 3D reconstruction and is not suitable for practical applications; the lidar method has point cloud data Sparse Disadvantages

Images