ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.
ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.
ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.
ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.
ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.
ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.
ShieldSwarm is an advanced set of robotics, of various form-factors, which work in collaboration to gather and process sensor data in the fastest, most accurate manner. This data collection allows our machine learning model(s) to analyze and process this data. The model(s) are able to create annotations from their analysis which are used to take further action and/or to augment the captured environment data.
The environment data and subsequent annotations are used to generate a 3D world which can be viewed in ShieldVision. The 3D world can be overlayed in a pass-through mode (MR/AR) or visualized and traversed in its entirety (VR) for post-event analysis.