Methods for performing anti-hebbian and hebbian (AHAH) based feature extraction of surface manifolds for compression
View Patent ↗Methods and systems for feature extraction of LIDAR surface manifolds. LIDAR point data with respect to one or more LIDAR surface manifolds can be generated. An AHAH-based feature extraction operation can be automatically performed on the point data for compression and processing thereof. The results of the AHAH-based feature extraction operation can be output as a compressed binary label representative of the at least one surface manifold rather than the point data to afford a high-degree of compression for transmission or further processing thereof. Additionally, one or more voxels of a LIDAR point cloud composed of the point data can be scanned in order to recover the compressed binary label, which represents prototypical surface patches with respect to the LIDAR surface manifold(s).
1. A method for feature extraction of surface manifolds, said method comprising:
locating an optical remote sensor that measures a distance to and/or other properties of a target by illuminating said target with light, wherein said optical remote sensor provides sensor data indicative of at least one surface manifold;
generating point data with respect to said at least one surface manifold; and
performing an Anti-Hebbian and Hebbian (AHAH)-based feature extraction operation on said point data for compression and processing thereof.
2. The method of claim 1 wherein said optical remote sensor comprises a Light Detection and Ranging (LIDAR) device.
3. The method of claim 1 further comprising scanning at least one voxel of a point cloud composed of said point data in order to recover said compressed binary label which represents prototypical surface patches with respect to said at least one surface manifold.
4. The method of claim 1 wherein said point data comprises LIDAR point data and said surface manifolds comprises LIDAR surface manifolds.
5. The method of claim 4 wherein said LIDAR point data is capable of being represented as a voxel with at least one active grid element and at least one inactive grid element, wherein a voxel grid element is considered active if said voxel grid element contains within a spatial boundary thereof at least one LIDAR point.
6. The method of claim 4 wherein said output of said voxel comprises a sparse binary activation vector including grid elements that are active.