首页|针对FSAC赛场环境的全局锥桶建图算法研究

针对FSAC赛场环境的全局锥桶建图算法研究

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针对中国大学生无人驾驶方程式(FSAC)赛车在高速循迹的"U型弯道"工况中,由于识别的锥桶有限,导致路径规划失效的问题,提出了一种基于激光雷达以及组合惯导系统的锥桶地图构建算法.该算法从当前帧激光点云中提取出锥桶中心点云,通过乘以雷达到组合惯导的标定矩阵和组合惯导实时解算的车辆在地图坐标系中的位姿矩阵,将激光坐标系下的锥桶点云投影到地图坐标系下,完成锥桶地图的构建与更新.将算法进行实车验证,3次实验结果表明:该算法的平均召回率为98.7%,平均精准度为98.1%,构建好的地图能够供给规划算法,拟合出赛道的全局最优路径进行加速跟踪,提高了赛车的感知预判能力和路径规划效率.
Research on global cone bucket mapping algorithm for FSAC field environment
To remedy the failures of path planning due to the limited identification of cones and barrels in the"U-bend"condition in FSAC racing car's high-speed tracking,this paper proposes a cone and barrel map construction algorithm based on lidar and integrated inertial navigation system.The algorithm extracts the cone barrel center point cloud from the laser point cloud of the current frame,and projects the cone barrel point cloud in the laser coordinate system to the map coordinate system by multiplying the calibration matrix from radar to integrated inertial navigation system and the pose matrix of the vehicle in the map coordinate system calculated in real time by the integrated inertial navigation system,completing the construction and updating the cone barrel map.The algorithm is verified on vehicles and the results of three experiments show its average recall rate reads 98.7%and its average accuracy stands at 98.1%.The constructed map provides the planning algorithm to fit the global optimal path of the track for accelerated tracking,improving the perception and prediction ability of the car and the efficiency of path planning.

FSAC racing carlidarintegrated inertial navigation systemcone bucket map

张要强、李刚、薛玉斌

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辽宁工业大学汽车与交通工程学院,辽宁锦州 121001

FSAC赛车 激光雷达 组合惯导系统 锥桶地图

国家自然科学基金项目辽宁省自然科学基金面上项目

516752572022-MS-376

2024

重庆理工大学学报
重庆理工大学

重庆理工大学学报

CSTPCD北大核心
影响因子:0.567
ISSN:1674-8425
年,卷(期):2024.38(1)
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