Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation,especially for future intelligent robots.In this paper,an improved pure pursuit control method is proposed for the path tracking control problem of a four-wheel independent steering robot.Based on the analysis of the four-wheel independent steering model,the kinematic model and the steering geometry model of the robot are established.Then the path tracking control is realized by considering the correlation between the look-ahead distance and the velocity,as well as the lateral error between the robot and the reference path.The experimental results demonstrate that the improved pure pursuit control method has the advantages of small steady-state error,fast response and strong robustness,which can effectively improve the accuracy of path tracking.
A method of environment mapping using laser-based light detection and ranging(LIDAR)is proposed in this paper.This method not only has a good detection performance in a wide range of detection angles,but also facilitates the detection of dynamic and hollowed-out obstacles.Essentially using this method,an improved clustering algorithm based on fast search and discovery of density peaks(CBFD)is presented to extract various obstacles in the environment map.By comparing with other cluster algorithms,CBFD can obtain a favorable number of clusterings automatically.Furthermore,the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm(ISODATA).
目的双目视差估计可以实现稠密的深度估计,因而具有重要研究价值。而视差估计和光流估计两个任务之间具有相似性,在两种任务之间可以互相借鉴并启迪新算法。受光流估计高效算法RAFT(recurrent all-pairs field transforms)的启发,本文提出采用单、双边多尺度相似性迭代查找的方法实现高精度的双目视差估计。针对方法在不同区域估计精度和置信度不一致的问题,提出了左右图像视差估计一致性检测提取可靠估计区域的方法。方法采用金字塔池化模块、跳层连接和残差结构的特征网络提取具有强表征能力的表示向量,采用向量内积表示像素间的相似性,通过平均池化得到多尺度的相似量,第0次迭代集成初始视差量,根据初始视差单方向向左查找多尺度的相似性得到的大视野相似量和上下文3种信息,而其他次迭代集成更新的视差估计量,根据估计视差双向查找多尺度的相似性得到的大视野相似量和上下文3种信息,集成信息通过第0次更新的卷积循环神经网络和其他次更新共享的卷积循环神经网络迭代输出视差的更新量,多次迭代得到最终的视差估计值。之后,通过对输入左、右图像反序和左右翻转估计右图视差,对比左、右图匹配点视差差值的绝对值和给定阈值之差判断视差估计置信度,从而实现可靠区域提取。结果本文方法在Sceneflow数据集上得到了与先进方法相当的精度,平均误差只有0.84像素,并且推理时间有相对优势,可以和精度之间通过控制迭代次数灵活平衡。可靠区域提取后,Sceneflow数据集上误差进一步减小到了历史最佳值0.21像素,在KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)双目测试数据集上,估计区域评估指标最优。结论本文方法对于双目视差估计具有优越性能,可靠区域提取方法能高效提取高精度估计区域,极大地提升了�
A new method based on adaptive Hessian matrix threshold of finding key SRUF( speeded up robust features) features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First,the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then,the standard local invariant feature extraction algorithm SRUF is analyzed,the Hessian Metrix is especially discussed,and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last,different dynamic object recognition experiments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for unmanned vehicle systems.
