Panoramic SWIR Imaging for Autonomous Vehicles

As autonomous systems, whether ground or air vehicles, navigate the 3D environment through space and time, they need to navigate accurately while avoiding collision risks with either stationary or moving objects in that environment. These risks will only increase as the use of autonomous systems, whether being operated for defense, commercial, or personal applications, increases. For example, at present, the most commonly used autonomous systems are small unmanned aerial vehicles (e.g., sUAV’s, sUAS’s, or drones). At present, NASA and the FAA are collaborating to develop equipment specifications and standards to enable safe air travel in a future world of Urban Air Mobility (UAM), in which UAVs traverse the NAS (National Air Space). In parallel, companies like Tesla are developing autopilot systems to enable autonomous piloting for ground vehicles. As cars typically traverse marked roads that cross 2 dimensional space, this is an easier problem. These same problems apply to military drones (e.g., the MQ-9 or Blue UAS Cleared), robotic combat vehicles, bi-pedal or quadrupedal forms (e.g., robodogs), autonomous ships, and other vehicles. However, in any case, current optically enabled navigation and object avoidance is limited by the camera technology, which typically collects visible images from multiple cameras. That approach is handicapped by image overlap between the cameras, parallax related image artifacts, and image stitching burdens that hinder real-time situational awareness of the local environment and events occurring therein.