Invention Grant
- Patent Title: Encoding LiDAR scanned data for generating high definition maps for autonomous vehicles
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Application No.: US15857417Application Date: 2017-12-28
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Publication No.: US10401500B2Publication Date: 2019-09-03
- Inventor: Lin Yang , Mark Damon Wheeler
- Applicant: DeepMap Inc.
- Applicant Address: US CA Palo Alto
- Assignee: DeepMap Inc.
- Current Assignee: DeepMap Inc.
- Current Assignee Address: US CA Palo Alto
- Agency: Fenwick & West LLP
- Main IPC: G06T9/20
- IPC: G06T9/20 ; G01S17/89 ; H04N19/17 ; G06K9/00 ; G01C21/30 ; G01C21/32 ; G01S17/02 ; G01S17/93 ; G05D1/02 ; G06F16/174 ; B60R11/04 ; G01C11/02

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.
Public/Granted literature
- US20180192059A1 ENCODING LIDAR SCANNED DATA FOR GENERATING HIGH DEFINITION MAPS FOR AUTONOMOUS VEHICLES Public/Granted day:2018-07-05
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