http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113378647-A
Outgoing Links
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|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d40285e27f4ed83b8728b0d477404958 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30252 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10044 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10028 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-66 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-73 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01S7-4802 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-73 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01S7-48 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-66 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 |
| filingDate | 2021-05-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_71ec45c141b31e48a726ffe5704cd0ee http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7329ff8256b76582c5c5350f41055d5b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7b5ccaf5c88f113a5568de9d53fc52c8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8b40c681d196d87ab83d3f19a8c17c77 |
| publicationDate | 2021-09-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113378647-A |
| titleOfInvention | Real-time rail obstacle detection method based on three-dimensional point cloud |
| abstract | The invention discloses a real-time rail obstacle detection method based on three-dimensional point cloud, which comprises the steps of processing three-dimensional point cloud sequence data acquired by a laser radar, firstly carrying out coordinate transformation on the point cloud, converting coordinates under an Euclidean coordinate system into coordinates under a spherical coordinate system, and putting each point in the point cloud into a certain conical voxel by using a conical voxelization down-sampling method to reduce the calculated amount of subsequent steps; and inputting the points subjected to downsampling into a local feature coding module, searching local point clouds by using K Nearest Neighbor (KNN), aggregating geometric features of the local point clouds, and connecting the mass center, coordinates of the nearest neighbor points, relative coordinates and Gaussian density features of the local point clouds into a vector. Connecting all local point cloud information into a matrix through traversal, and acquiring high-dimensional local feature information of each local point cloud through MLP and maximum pooling; and finally, realizing real-time track identification of the single-frame image by utilizing multi-scale three-dimensional sparse convolution through a plurality of down-sampling and up-sampling modules. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114446092-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114446092-B |
| priorityDate | 2021-05-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID5281069 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID451807912 |
Total number of triples: 26.