【摘要】：為了解決激光雷達掃描遠距離運動車輛產(chǎn)生的點云稀疏導(dǎo)致位姿特征難以提取的問題,提出了一種遠距離運動車輛位姿估計方法。首先利用時空連續(xù)性提取遠距離運動車輛。然后利用最小二乘擬合得到稀疏點云水平面二維投影近似擬合直線對,依次在不同角度的垂直正交直線對上對稀疏點云的二維投影進行一維向量估計的裝箱過程,基于目標(biāo)車輛與激光雷達間相對位置的觀測角函數(shù)最大化匹配濾波響應(yīng),進而利用全局優(yōu)化算法對投影點概率分布與匹配濾波運算得到的代價函數(shù)作離散卷積,尋優(yōu)比較得到單幀擬合最優(yōu)矩形。最后結(jié)合連續(xù)幀平移約束進行多幀擬合,優(yōu)化當(dāng)前幀目標(biāo)車輛擬合矩形的位姿。利用仿真和真實場景下采集的目標(biāo)車輛點云數(shù)據(jù)進行算法驗證分析。結(jié)果表明:在點云稀疏的情況下,當(dāng)遠距離目標(biāo)車輛做直線運動時,提出的多幀擬合方法得到的位姿參數(shù)均方根誤差低于單幀擬合和已有的RANSAC擬合方法;當(dāng)遠距離目標(biāo)車輛做曲線運動時,提出的單幀擬合和多幀擬合方法得到的位姿估計結(jié)果較為接近,且誤差明顯低于已有的RANSAC擬合方法;對于不同相對距離下采集的目標(biāo)車輛點云,提出的單幀擬合和多幀擬合位姿估計方法的適應(yīng)性優(yōu)于已有的RANSAC擬合方法。
[Abstract]:In order to solve the problem that the point cloud sparsity caused by the remote moving vehicle scanned by lidar makes it difficult to extract the pose feature, a method of position and attitude estimation for long range moving vehicle is proposed. First, the spatiotemporal continuity is used to extract the long distance moving vehicle. Then the least square fitting is used to get the approximate fitting line pair of sparse point cloud plane two-dimensional projection, and the packing process of one dimensional vector estimation of the two-dimensional projection of sparse point cloud is carried out on the vertical orthogonal straight line pairs of different angles in turn. Based on the maximum matched filtering response based on the relative position between the vehicle and the lidar, the global optimization algorithm is applied to the discrete convolution of the probability distribution of the projection point and the cost function obtained by the matched filtering operation. Single frame fitting optimal rectangle is obtained by optimization comparison. Finally, multiple frames are fitted with successive frame translation constraints to optimize the position and orientation of the current frame target vehicle fitting rectangle. The algorithm is verified and analyzed by using the point cloud data of the target vehicle collected in the simulation and real scene. The results show that the root mean square error of pose parameters obtained by the multi-frame fitting method is lower than that of single frame fitting method and RANSAC fitting method when the vehicle is moving in a straight line when the point cloud is sparse. When the long distance target vehicle moves on the curve, the position and pose estimation results obtained by the single frame fitting method and the multi frame fitting method are close, and the error is obviously lower than that of the existing RANSAC fitting method. For the point clouds of target vehicles collected at different relative distances, the proposed methods of single-frame fitting and multi-frame fitting are more adaptable than the existing RANSAC fitting methods.
【作者單位】： 長安大學(xué)汽車學(xué)院;西安工業(yè)大學(xué)機電工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(61473046) 中央高校基本科研業(yè)務(wù)費專項資金項目(310822151028,310822172001) 陜西省自然科學(xué)基金項目(2016JQ5096) 長江學(xué)者和創(chuàng)新團隊發(fā)展計劃項目(IRT1286)
【分類號】：TN958.98;U463.67

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