Encoding LiDAR scanned data for generating high definition maps for autonomous vehicles
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United States of America Patent
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Sep 12, 2023
Grant Date -
Nov 24, 2022
app pub date -
Dec 28, 2021
filing date -
Dec 30, 2016
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Abstract
Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.

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Patent Owner(s)
Patent Owner | Address | |
---|---|---|
NVIDIA CORPORATION | 2788 SAN TOMAS EXPRESSWAY SANTA CLARA 95051 |
International Classification(s)
- H04N:PICTORIAL COMMUNICATION, e.g. TELEVISION
- G06T:IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F:ELECTRIC DIGITAL DATA PROCESSING
- B60R:VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT ....
- G05D:SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC....
- G01S:RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMI....
- G01C:MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING....

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Inventor(s)
Inventor Name | Address | # of filed Patents | Total Citations |
---|---|---|---|
Wheeler, Mark Damon | Saratoga, US | 68 | 1723 |
Yang, Lin | San Carlos, US | 1008 | 9000 |
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