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.

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.

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.

Abstract: Systems and methods allow for devices, such as sensory systems and mixed reality content consuming systems installed within a vehicle, to discover each other, share multidimensional realities, inherit context for mixed reality content, and/or coordinate whilst providing a mixed reality user experience. A discovery service can receive information from multiple devices and correlate digital and physical characteristics thereof to enable devices to discover each other. Characteristics of a device that may be used for pairing may include its orientation physically, the observable reality of a device, the state-machine of the device, the intentions of the device whilst operating autonomously or with external assistance, the location of a device, the temporal/spatial information describing the device's movement over time in a multi-dimensional reality or any unique identifiers contained within or generated by the device.

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.

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, 5910) 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.

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.

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.

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.

Abstract: Systems and methods allow for devices, such as sensory systems and mixed reality content consuming systems installed within a vehicle, to discover each other, share multidimensional realities, inherit context for mixed reality content, and/or coordinate whilst providing a mixed reality user experience. A discovery service can receive information from multiple devices and correlate digital and physical characteristics thereof to enable devices to discover each other. Characteristics of a device that may be used for pairing may include its orientation physically, the observable reality of a device, the state-machine of the device, the intentions of the device whilst operating autonomously or with external assistance, the location of a device, the temporal/spatial information describing the device's movement over time in a multi-dimensional reality or any unique identifiers contained within or generated by the device.

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.

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.

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.

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.

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.