Abstract
A single VLP-16 LiDAR estimation method based on a single-frame 3D laser point cloud is proposed to address the problem of estimating negative obstacles’ geometrical features in structured environments. Firstly, a distance measurement method is developed to determine the estimation range of the negative obstacle, which can be used to verify the accuracy of distance estimation. Secondly, the 3D point cloud of a negative obstacle is transformed into a 2D elevation raster image, making the detection and estimation of negative obstacles more intuitive and accurate. Thirdly, we compare the effects of a StatisticalOutlierRemoval filter, RadiusOutlier removal, and Conditional removal on 3D point clouds, and the effects of a Gauss filter, Median filter, and Aver filter on 2D image denoising, and design a flowchart for point cloud and image noise reduction and denoising. Finally, a geometrical feature estimation method is proposed based on the elevation raster image. The negative obstacle image in the raster is used as an auxiliary line, and the number of pixels is derived from the OpenCV-based Progressive Probabilistic Hough Transform to estimate the geometrical features of the negative obstacle based on the raster size. The experimental results show that the algorithm has high accuracy in estimating the geometric characteristics of negative obstacles on structured roads and has a practical application value for LiDAR environment perception research.
Highlights
Ambient sensing is the critical technology for ALV (Autonomous Land Vehicles) /UGV (Unmanned Ground Vehicles) to achieve autonomous navigation in outdoor environments
Rankin et al [4] further coupled nighttime negative obstacle detection with thermic featurebased cues and geometric cues based on stereo distance data, using edge detection to generate closed contour candidate negative obstacle regions and geometrical filtering, to determine whether they are located in the ground plane
This paper proposes a single multi-line radar method to improve the accuracy of negative obstacle geometry feature estimation with lower hardware cost to address the above problems
Summary
Ambient sensing is the critical technology for ALV (Autonomous Land Vehicles) /UGV (Unmanned Ground Vehicles) to achieve autonomous navigation in outdoor environments. The method is simple, computationally convenient, and low-cost, breaking the previous costly methods such as dual multi-beam LiDAR and joint calibration of LiDAR and camera or combination of IMU [13] inertial guidance and LiDAR, and using a single VLP-16 LiDAR to accurately estimate the geometry of negative obstacles on the horizontal ground by using virtual images generated from the data and some geometric operations. This method saves hardware costs and frees up more space and money to implement driverless technology
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