The embodiment of the invention relates to a quick barrier detection method. The method comprises the steps that a two-dimensional grid map is established on the basis of point cloud data of a three-dimensional radar, the attributes of grids are calculated to determine impassable areas, points of which the scanning line gradient values exceed predetermined threshold values in the impassable areas are determined as barrier points, and correlation clustering is conducted on the barrier points by traversing the grid map.

Quick barrier detection method

The invention discloses an obstacle recognition method and device for vehicles. The method in one specific embodiment comprises the steps of: acquiring point cloud data of a target area by using a laser radar sensor and acquiring image data of the target area by using an image acquisition device; recognizing obstacle information in the point cloud data by using a preset point cloud recognition model, and marking the recognition result to obtain a first marked result; recognizing obstacle information in the image data by using a preset image recognition model, and marking the recognition result to obtain a second marked result; comparing the first marked result with the second marked result to detect whether the both are same; and responding to the difference of the both, determining an accurate marked result in the first marked result and the second marked result, and outputting the accurate marked result. The method and the device realize quick and accurate recognition of the accurate marked result, do not need artificial marking in the whole course, and thus improve the marking efficiency.

Obstacle recognition method and device for vehicles

An embodiment of the invention provides a method and device for detecting lane lines on a road surface. The method includes the steps of: collecting an RGB image of a road surface and a point cloud intensity map of the road surface; performing lane line detection on the RGB image and the point cloud intensity map respectively, thereby obtaining image three-dimensional lane lines in the RGB image and point cloud three-dimensional lane lines in the point cloud intensity map; and based on a preset lane line fusing strategy, fusing the image three-dimensional lane lines and the point cloud three-dimensional lane lines, thereby obtaining three-dimensional lane lines of the road surface, and outputting the three-dimensional lane lines. By fusion of a lane line detection result of the RGB image and a lane line detection result of the point cloud intensity map, shielding of the road surface lane lines by other vehicles and wear of the lane lines on the road surface and road surface direction arrows can be effectively resisted in most cases, integrity and precision of geometric outlines of the obtained three-dimensional lane lines of the road surface are improved, and in addition, manpower cost is saved.

Method and device for detecting lane lines on road surface

The invention discloses a ground point cloud segmentation method based on a three-dimensional laser radar. The ground point cloud segmentation method comprises the steps of: 1) acquiring polar coordinate data of three-dimensional laser radar scanning point cloud in the surrounding environment of a vehicle and converting the polar coordinate data to a local rectangular coordinate system; 2) correcting radar data by utilizing a vehicle-mounted IMU and an odometer; 3) constructing a polar coordinate grid map, and extracting an extension vertex in each grid according to vertical continuity of point cloud distribution in the grids; 4) extracting ground points in non-marginal grids according to height attributes of the extension vertexes and a ground smooth consistency criterion, and adopting a 3sigma criterion for further extraction of ground points in marginal grids. Compared with the prior art, the ground point cloud segmentation method can reduce ground extraction errors caused by motion of the vehicle, avoids the occurrence of over-segmentation and under-segmentation, is high in precision and high in reliability, has high real-time performance and can be widely used in the field of radar-based environment sensing technologies.

Ground point cloud segmentation method based on three-dimensional laser radar

The invention discloses an automatic extraction technology of street lamp poles based on vehicle laser scanning point clouds. The technology comprises the following steps: carrying out pre-processing including filtering ground points and dividing non-ground points into super-voxel sets; positioning all pole-shaped objects in the scene which might be street lamp poles; carrying out position-oriented segmentation and obtaining the pole-shaped objects; using extracted pole features and global features to describe the pole-shaped objects obtained through segmentation; training random forests and classifier SVMs with manually-labeled training samples, and using the trained random forests and classifier SVMs to classify the pole-shaped objects segmented through the step S3 to identify the street lamp poles. The algorithm of the invention has good robustness under complex environment such as incomplete or obscured street light, and the time complexity of the algorithm is very low, and thus the algorithm can be quickly applied to point clouds of large-scale scenes.

Automatic extraction technology of street lamp poles based on vehicle laser scanning point clouds

The embodiment of the invention discloses a laser-point-cloud-based urban road identification method and apparatus. The method comprises: a corresponding road edge model is constructed according to laser point clouds collected by a laser sensor; a height of a mobile carrier with the laser sensor is determined and a corresponding road surface model is constructed based on the height and the laser point; and according to the road edge model and the road surface model, a road surface point cloud and a road edge point cloud in the laser point cloud are eliminated, the rest of laser point clouds are segmented by using a point cloud segmentation algorithm, and an object corresponding to the a segmentation result is identified. The height of the mobile carrier is estimated based on the laser point cloud and the road surface model corresponding to the laser point cloud is constructed by using the height, so that the construction efficiency and accuracy of the road surface model are improved. Therefore, the identification efficiency and accuracy of the corresponding object are improved.

Laser-point-cloud-based urban road identification method and apparatus

Embodiments of the present disclosure disclose a method and apparatus for urban road recognition based on a laser point cloud. The method comprises: constructing a corresponding road edge model according to the laser point cloud acquired by a laser sensor; determining a height of a mobile carrier provided with the laser sensor and constructing a corresponding road surface model based on the height and the laser point cloud; eliminating a road surface point cloud and a road edge point cloud in the laser point cloud according to the road edge model and the road surface model, segmenting a remaining laser point cloud using a point cloud segmentation algorithm, and recognizing an object corresponding to a segmenting result. By estimating the height of the mobile carrier according to the laser point cloud and constructing a corresponding road surface model using the height, the efficiency and accuracy of constructing the road surface model are improved, thereby improving the efficiency and accuracy of recognizing corresponding objects.

Method and apparatus for urban road recognition based on laser point cloud, storage medium, and device

본 개시의 실시예는 레이저 포인트 클라우드 기반의 도시 도로 인식 방법 및 장치를 개시한다. 상기 방법은, 레이저 센서로 수집한 레이저 포인트 클라우드에 기반하여 상응한 도로변 모델을 구축하는 단계; 상기 레이저 센서가 설치된 이동 캐리어의 높이를 확정하고, 상기 높이 및 레이저 포인트 클라우드에 기반하여 상응한 노면 모델을 구축하는 단계; 및 상기 도로변 모델 및 상기 노면 모델에 기반하여, 상기 레이저 포인트 클라우드 중의 노면 포인트 클라우드 및 도로변 포인트 클라우드를 제거하고, 포인트 클라우드 분할 알고리즘을 이용하여 나머지 레이저 포인트 클라우드에 대해 분할을 진행하며, 분할 결과에 대응되는 물체를 인식하는 단계;를 포함한다. 레이저 포인트 클라우드에 기반하여 이동 캐리어의 높이를 추산하고, 상기 높이를 이용하여 레이저 포인트 클라우드에 대응되는 노면 모델을 구축함으로써, 노면 모델의 구축 효율 및 정확도를 향상시키고, 나아가 상응한 물체의 인식 효율 및 정확도를 향상시킨다.

레이저 포인트 클라우드 기반의 도시 도로 인식 방법, 장치, 저장 매체 및 기기

Disclosed are an urban road recognition method, apparatus, storage medium and device based on a laser point cloud. The method comprises: constructing a corresponding road line model according to a laser point cloud captured by a laser sensor; determining the height of a mobile carrier which is provided with the laser sensor, and constructing the corresponding road surface model according to the height and the laser point cloud; and according to the road line model and the road surface model, eliminating the road surface point cloud and the road line point cloud in the laser point cloud, segmenting the remaining laser point clouds using a point cloud segmentation algorithm, and recognizing the object corresponding to a segmentation result. By estimating the height of a mobile carrier according to a laser point cloud and constructing a road surface model corresponding to the laser point cloud using the height, the construction efficiency and accuracy of the road surface are improved, thereby improving the recognition efficiency and accuracy of a corresponding object.

Urban road recognition method, apparatus, storage medium and device based on laser point cloud

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Laser-point-cloud-based urban road identification method and apparatus

Method and apparatus for urban road recognition based on laser point cloud, storage medium, and device

레이저 포인트 클라우드 기반의 도시 도로 인식 방법, 장치, 저장 매체 및 기기

Urban road recognition method, apparatus, storage medium and device based on laser point cloud