Grid-based contour line segment feature extraction algorithm

Abstract

The invention relates to the technical field of mobile robots. More specifically, the present invention relates to a grid-based contour line segment feature extraction algorithm. The method comprisesthe steps: clustering data points, sorting the data points in each cluster, segmenting the data points in each cluster, performing line segment fitting on the data points in each data segment, takingthe first three items as the method for searching the line segment support area provided by the invention, and fitting line segments in the support area by using a least square method in combination with the method. According to the algorithm, when the line segment supporting area is searched, a large memory and long calculation time are not needed, and when the line segment is fitted, the fittingmethod is simple. According to the method, the performance requirement on the rotary laser scanner can be reduced, and the influence of noisy points on the extraction effect is reduced. Moreover, parameters such as thresholds do not need to be set, the performance requirement on the rotary laser scanner is reduced, the influence of noisy points on the extraction effect is reduced, and the extracted line segment is high in resolution, robustness, efficiency and real-time performance.

Description

technical field [0001] The present invention relates to the technical field of mobile robots, and more specifically, to a grid-based contour segment feature extraction algorithm. Background technique [0002] The problem of simultaneous localization and mapping (SLAM: simultaneous localization and mapping) has always been an important direction of mobile robot research, and grid maps are a common method for map construction in SLAM problems. Compared with other environmental features such as edges, corners, regions, and ridges, line segments are mathematically simple middle-level descriptors, which can not only describe many environmental objects, but also have low extraction complexity. Therefore, line segment feature extraction on raster maps is a good research direction in SLAM problems. [0003] At present, the line segment feature extraction methods mainly include PDBS (Point-Distance-Base Segmentation), SEF (Successive Edge Following), LT (Line Tracking), IEPF (Iterat...

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Problems solved by technology

[0003] At present, the line segment feature extraction methods mainly include PDBS (Point-Distance-Base Segmentation), SEF (Successive Edge Following), LT (Line Tracking), IEPF (Iterative End PointFit), SM (Split-and-Merge), etc. However, These extraction methods have high requirements on the sampling speed, data validity, and continuous type of the sensor, are easily affected by noise, and depend on an appropriate threshold. The complexity and variability of the environment make threshold selection difficult.

Among them: PDBS algorithm and SEF algorithm depend on the continuity of laser scanning data. If there is data loss or noise, it is easy to misjudgment, and because laser data has different detection accuracy for nearby obstacles and distant obstacles, the algorithm The setting of the middle threshold dth often takes care of one thing and loses another; the LT algorithm easily divides the data points of the next line segment into the previous line segment, resulting in misjudgment at the endpoint of the line segment; the IEPF algorithm is easily affected by noise, resulting in too many line segments, and due to Due to the complexity and variability of the working environment, it is difficult to select the threshold; the SM algorithm is sensitive to noise and threshold, and it is easy to get wrong results

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