This tool uses the random sample consensus (RANSAC) method to identify points within a LiDAR point cloud that belong to planar surfaces. RANSAC is a common method used in the field of computer vision to identify a subset of inlier points in a noisy data set containing abundant outlier points. Because LiDAR point clouds often contain vegetation points that do not form planar surfaces, this tool can be used to largely strip vegetation points from the point cloud, leaving behind the ground returns, buildings, and other points belonging to planar surfaces. If the `classify` flag is used, non-planar points will not be removed but rather will be assigned a different class (1) than the planar points (0).

The algorithm selects a random sample, of a specified size (`num_samples`) of the points from within the neighbourhood (`radius`) surrounding each LiDAR point. The sample is then used to parameterize a planar best-fit model. The distance between each neighbouring point and the plane is then evaluated; inliers are those neighbouring points within a user-specified distance threshold (`threshold`). Models with at least a minimum number of inlier points (`model_size`) are then accepted. This process of selecting models is iterated a number of user-specified times (`num_iter`).

One of the challenges with identifying planar surfaces in LiDAR point clouds is that these data are usually collected along scan lines. Therefore, each scan line can potentially yield a vertical planar surface, which is one reason that some vegetation points remain after applying the RANSAC plane-fitting method. To cope with this problem, the tool allows the user to specify a maximum planar slope (`max_slope`) parameter. Planes that have slopes greater than this threshold are rejected by the algorithm. This has the side-effect of removing building walls however.

## References

Fischler MA and Bolles RC. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381–395.

## See Also

lidar_segmentation, lidar_ground_point_filter

## Function Signature

`def lidar_ransac_planes(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, classify: bool = False, only_last_returns: bool = False) -> Lidar: ...`

## Project Links

WbW Homepage User Manual Support WbW