We would have already worked with ROS where it will be involved with SLAM, Navigation etc. But when we need to work with any Artificial Intelligence which involves high GPU access were the entry level SBC’s like Raspberry Pi, Beaglebone Black, Odroid U3 and many more can’t afford this GPU capability. This is the right entry into the ROS hardware ecosystem of Jetson Nano board which is mainly used for A.I. & run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing.
While checking up the cost of Jetson Nano which seems to be very affordable when comparing to other Jetson boards with these capabilities.
|CPU||Quad-core ARM A57 @ 1.43 GHz|
|Memory||4 GB 64-bit LPDDR4 25.6 GB/s|
|Storage||microSD (not included)|
|Video Encode||4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)|
|Video Decode||4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265)|
|Camera||1x MIPI CSI-2 DPHY lanes|
|Connectivity||Gigabit Ethernet, M.2 Key E|
|Display||HDMI 2.0 and eDP 1.4|
|USB||4x USB 3.0, USB 2.0 Micro-B|
|Others||GPIO, I2C, I2S, SPI, UART|
|Mechanical||100 mm x 80 mm x 29 mm|
To know more details about the Jetson Nano, you can visit official link: https://developer.nvidia.com/embedded/buy/jetson-nano-devkit
To get started ROS on Jetson Nano you can find the best self understandable link: https://www.stereolabs.com/blog/getting-started-with-jetson-nano/