Developing vision systems with dissimilar sensors
Drones, intelligent cars and augmented or virtual reality (AR/VR) headsets all use multiple image sensors, often of different types, to capture data about their operating environment. To supply the image data the system needs, each sensor requires a connection to the system’s application processor (AP), which presents the design challenge of developing vision systems with dissimilar sensors for embedded engineers.
First, APs have a finite number of I/O ports available for connecting with sensors, so I/O ports must be carefully allocated to ensure all discrete components requiring a connection to the AP have one. Secondly, drone and AR/VR headsets have small form factors and use batteries for power, so components used in these applications must be as small and power efficient as possible.
One solution to the AP’s shortage of I/O ports is the use of Virtual Channels, as defined in the MIPI Camera Serial Interface-2 (CSI-2) specification, which can consolidate up to 16 different sensor streams into a single stream that can then be sent to the AP over just one I/O port. The hardware platform of choice for a Virtual Channel implementation is the field programmable gate array (FPGA).
Alternative hardware platforms take a long time to design, and may not have the low power performance needed for applications like drones or AR/VR headsets. Some would argue that FPGAs have too large a footprint and consume too much power to be a feasible platform for Virtual Channel support. But advances in semiconductor design and manufacturing are enabling a new generation of smaller, more power efficient FPGAs.
The growing demand among consumers for drones, intelligent cars and AR/VR headsets is driving tremendous growth in the sensor market. Semico Research sees automotive (27% CAGR), drone (27% CAGR) and AR/VR headset (166% CAGR) applications as the primary demand drivers for sensors, and forecasts semiconductor OEMs will ship over 1.5 billion image sensors a year by 2022.
The applications mentioned above require multiple sensors to capture data about the application’s operating environment. For example, an intelligent car could use several high-definition image sensors for the rear-view and surround cameras, a LIDAR sensor for object detection and a radar sensor for blind spot monitoring.
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