Patent ID: 12190597

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG.1shows one example of a 3D point cloud20which is processed based on a method according to the present invention and detected with the aid of a LIDAR sensor10of a road vehicle. The points of 3D point cloud20are projected onto a reference plane40whose orientation essentially corresponds to an orientation of a roadway surface in the immediate surroundings of LIDAR sensor10. The representation inFIG.1corresponds to a top view onto reference plane40. LIDAR sensor10is connected, in terms of information technology, to an evaluation unit92, in this case a microcontroller, of a control unit90of the road vehicle. Evaluation unit92is configured to receive the 3D point cloud detected by LIDAR sensor10and to process it according to the method according to the present invention. A result of the processing by evaluation unit92is transferred, for example, to a surroundings recognition system, which may also be an integral component of control unit90.

It shall generally be pointed out that, for reasons of a simplified representation, only individual elements are representatively denoted by reference numerals inFIG.1. These reference numerals implicitly also apply accordingly for identically represented further elements inFIG.1.

With the aid of evaluation unit92, 3D point cloud20is initially divided into a plurality of square cells50abutting one another without interruption. Thereafter, those points of 3D point cloud20are considered which are situated within a first predefined distance30of 15 m from LIDAR sensor10. For all points within first distance30, it is checked in each case whether a shortest distance of these points from reference plane40does not exceed a third predefined distance of 0.3 m. In the event that the third distance is not exceeded for a respective point, the respective point is established as a starting ground point22. All ground points24established as starting ground points22are marked here by a single ring which surrounds starting ground points22.

Moreover, points of 3D point cloud10are identified with the aid of evaluation unit92as obvious object points26(i.e., points which were obviously caused by an object in the surroundings) which are situated within first distance30and whose smallest distance from reference plane40is above a predefined object minimum distance of 0.3 m and whose vector products72with directly adjoining points in each case exceed a third predefined angular deviation of 45° with respect to a normal45of reference plane40. These obvious object points26are in each case identified by two concentric rings which surround the obvious object points26.

Thereafter, those cells50within first distance30which have a required minimum number of corresponding starting ground points22, the minimum number corresponding to a value of 5 here, are established as starting cells52.

Thereafter, a cell plane54is ascertained for each starting cell52, cell plane54approximating the position of starting ground points22in the respective cell50in such a way that a sum of the squared deviations of the shortest distances of starting ground points22of the cell from cell plane54is minimized.

Thereupon, candidate cells60abutting starting cells52are ascertained, which must each include a minimum number of two points here to be considered candidate cells60. For each candidate cell60, an estimated cell plane62is now ascertained from the previously ascertained cell planes54of adjoining starting cells52in that an average plane is ascertained from all directly abutting, adjoining cell planes54.

In each candidate cell60, cell plane candidate points64are now ascertained whose smallest distance from estimated cell plane62does not exceed a second predefined distance66of 0.3 m.

Thereafter, a cell plane54is ascertained for each candidate cell60, cell plane54approximating the position of cell plane candidate points64in the respective candidate cell60in such a way that a sum of the squared deviations of the shortest distances of cell plane candidate points64of candidate cell60from cell plane54is minimized.

Proceeding from the previously processed candidate cells60, candidate cells60which abut these candidate cells60and which have not yet been processed are identified according to the above description, and above-described method steps are employed to also ascertain cell planes54and ground points24corresponding thereto for these candidate cells60. This is continued until all cells50of reference plane40have been processed.

In the event that the boundary conditions for ascertaining cell planes54and ground points24for the respective cells50(starting cells52and candidate cells60) should not be met, an estimated cell plane62is used for these cells50, which is ascertained from all existing cell planes54in adjoining cells50(starting cells52and candidate cells60).

Thereafter, those points of 3D point cloud20are classified as object points26which are situated within the first predefined distance30from sensor10, whose smallest distance from cell plane54exceeds a predefined object minimum distance, and whose vector products72with directly adjoining points exceed a third predefined angular deviation of 45° with respect to a normal of cell plane54.

FIG.2shows one example of a candidate cell60and points of 3D point cloud20corresponding to candidate cell60. Four points of 3D point cloud20are shown, which in a projection onto a reference plane40(not shown), which represents a ground surface and which is divided into a plurality of mutually abutting cells50, are situated within cell50of reference plane40which corresponds to candidate cell60. An estimated cell plane62of candidate cell60was ascertained based on cell planes54which exist in cells50which abut candidate cell60. Now those points of the 3D point cloud whose distance from estimated cell plane62does not exceed a second predefined distance66of 0.2 m are identified as cell plane candidate points64. This applies both to points above and below the estimated cell plane62. Three of these points meet this distance criterion here, while one of the points does not meet this criterion and accordingly is rated as a non-classified point28. Based on the points identified as cell plane candidate points64, thereafter a final, i.e. non-estimated, cell plane54is ascertained for this candidate cell60.

FIG.3shows one example of an iterative cell growth according to the present invention along a ground surface.FIG.3may be considered a detail fromFIG.1, further cells50of reference plane40already having been processed here compared toFIG.1. To avoid repetition, only differences compared toFIG.1are therefore described hereafter. Shown is a starting cell50and four candidate cells60, for each of which a cell plane54was ascertained. For each of these cells50,60, a local normal vector74was ascertained in each case, which is an average vector which was ascertained based on all vector products72which result from possible combinations of vectors70between ground points within a cell50,60. Local normal vectors74are subsequently used to ascertain an angular deviation between the respective local normal vectors74and a normal vector45of reference plane40. If the ascertained angular deviation is greater than a predefined second angular deviation of 15°, the respective cell plane,54,62is not taken into consideration in a downstream processing since it is then not regarded as a reliable ground cell. Moreover, an estimated cell plane62of a candidate cell60to be instantaneously processed is shown inFIG.3.