Real Time Machine Vision and Point-Cloud Analysis For Remote Sensing and Vehicle Control

• US 20160221592A1
• Filed: 01/20/2016
• Published: 08/04/2016
• Est. Priority Date: 11/27/2013
• Status: Active Grant

• Current Assignee: Condor Acquisition SUB II Incorporated (Luminar Technologies, Inc.)
• Original Assignee: Solfice Research Inc. (Luminar Technologies, Inc.)
• Inventors: Gupta, Anuj, Harvey, Scott, Creadore, Jason, Mills, Graham, Puttagunta, Shanmukha Sravan, Chraim, Fabien
• Granted Patent: US 9,796,400 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B61L 2205/04 Satellite based navigation systems, e.g. global positioning system [GPS]
• CPC Class Codes: B61L 23/041 Obstacle detection
• CPC Class Codes: B61L 23/34 for indicating the distance between vehicles or trains by the transmission of signals therebetween
• CPC Class Codes: B61L 25/025 Absolute localisation, e.g. providing geodetic coordinates
• CPC Class Codes: B61L 25/04 Indicating or recording train identities
• CPC Class Codes: B61L 27/04 Automatic systems, e.g. controlled by train; Change-over to manual control

Abstract

Methods and apparatus for real time machine vision and point-cloud data analysis are provided, for remote sensing and vehicle control. Point cloud data can be analyzed via scalable, centralized, cloud computing systems for extraction of asset information and generation of semantic maps. Machine learning components can optimize data analysis mechanisms to improve asset and feature extraction from sensor data. Optimized data analysis mechanisms can be downloaded to vehicles for use in on-board systems analyzing vehicle sensor data. Semantic map data can be used locally in vehicles, along with onboard sensors, to derive precise vehicle localization and provide input to vehicle to control systems.

Claims

1. An apparatus for identifying assets within point-cloud survey data, the apparatus comprising:

a front end component accessible via a digital communications network for receiving a point-cloud dataset;

a data storage component, the data storage component storing the point-cloud dataset and subdividing the point-cloud dataset into a plurality of data chunks;

a processing unit comprising a compute cluster, the processing unit receiving streamed data chunks from the data storage component and applying one or more analysis mechanisms to each data chunk to extract asset information; and

a map generator combining asset information extracted from the data analysis mechanisms into an output map.

2. The apparatus of claim 1, in which each data chunk comprises one or more tiles of point-cloud data.

3. The apparatus of claim 2, in which each tile comprises a subset of point-cloud data within a rectangular column extending lengthwise along the Earth'"'"'s gravity vector.

4. The apparatus of claim 3, in which each data chunk contains a number of contiguous tiles optimized to achieve a target data chunk size.

5. The apparatus of claim 1, in which the map generator further comprises an annotation integrity verifier comparing asset information in an output map with asset information in one or more prior output maps corresponding to a common local environment, to generate a notification when discrepancies are detected.

6. The apparatus of claim 1, further comprising:

a compression mechanism operating to compress the point-cloud data prior to storage within the data storage component; and

a decompression mechanism operating to decompress the point-cloud data prior to application of the analysis mechanisms by the processing unit;

whereby the compression mechanism modulates its compression ratio to balance a data retrieval rate from the data storage component, with a data processing rate achievable by the processing unit.