The invention relates to a semi-static indoor parking scene visual positioning method, and belongs to the technical field of automatic driving, and the method comprises the following steps: extracting an aerial view parking space end point, and carrying out the tracking optimization of the parking space end point, and obtaining a position under a world coordinate system; constructing parking space line segments in a map by combining related parking space information; other road surface mark point clouds are used for mapping in a point cloud mode and used for estimating the pose of the vehicle; using the parking space endpoints as map information to be bound with the key frames; loading a prior map, obtaining a road surface mark point cloud in the aerial view through semantic segmentation, matching a parking space line point cloud with a nearest parking space line segment in the map, and matching other road surface mark point clouds with nearest points in the map; residual errors are constructed through the point-to-line distance and the point-to-point distance, the two residual errors are added, and the vehicle pose is solved through iterative optimization; and constructing a covariance matrix according to the average distance residual error of each matching point, and introducing an inertial measurement unit to realize error Kalman filtering sensor fusion positioning.
本发明涉及一种半静态室内泊车场景视觉定位方法,属于自动驾驶技术领域,包括以下步骤:提取鸟瞰图停车位端点,通过对停车位端点跟踪优化得到其世界坐标系下的位置;再结合相关车位信息在地图中构建停车位线段;其他路面标记点云使用点云的方式建图并用于估计车辆位姿;将停车位端点作为地图信息和关键帧绑定;加载先验地图,通过语义分割获取鸟瞰图中的路面标记点云,将停车位线点云和地图中最近停车位线段匹配,其他路面标记点云和地图中最近点匹配;分别通过点到线的距离和点到点的距离构建残差,将两种残差相加并迭代优化求解车辆位姿;根据每个匹配点的平均距离残差构建协方差矩阵,引入惯性测量单元实现误差卡尔曼滤波传感器融合定位。
Semi-static indoor parking scene visual positioning method
半静态室内泊车场景视觉定位方法
2024-03-29
Patent
Elektronische Ressource
Chinesisch
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