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.
Type:
Grant
Filed:
October 23, 2017
Date of Patent:
February 4, 2020
Assignee:
SOLFICE RESEARCH, INC.
Inventors:
Shanmukha Sravan Puttagunta, Fabien Chraim, Anuj Gupta, Scott Harvey, Jason Creadore, Graham Mills
Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.
Type:
Grant
Filed:
September 29, 2018
Date of Patent:
November 26, 2019
Assignee:
SOLFICE RESEARCH, INC.
Inventors:
Shanmukha Sravan Puttagunta, Fabien Chraim, Scott Harvey
Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.
Type:
Grant
Filed:
March 15, 2017
Date of Patent:
July 30, 2019
Assignee:
Solfice Research, Inc.
Inventors:
Shanmukha Sravan Puttagunta, Fabien Chraim, Scott Harvey
Abstract: A system, method, and apparatus are disclosed for a machine vision system that incorporates hardware and/or software, remote databases, and algorithms to map assets, evaluate railroad track conditions, and accurately determine the position of a moving vehicle on a railroad track. One benefit of the invention is the possibility of real-time processing of sensor data for guiding operation of the moving vehicle.