RT-MonoDepth: Real-time Monocular Depth Estimation on Embedded Systems
Cheng Feng Zhen Chen Congxuan Zhang Weiming Hu Bing Li Feng Lu
Depth sensing is a crucial function of unmanned aerial vehicles and
autonomous vehicles. Due to the small size and simple structure of monocular
cameras, there has been a growing interest in depth estimation from a single
RGB image. However, state-of-the-art monocular CNN-based depth estimation
methods using fairly complex deep neural networks are too slow for real-time
inference on embedded platforms. This paper addresses the problem of real-time
depth estimation on embedded systems. We propose two efficient and lightweight
encoder-decoder network architectures, RT-MonoDepth and RT-MonoDepth-S, to
reduce computational complexity and latency. Our methodologies demonstrate that
it is possible to achieve similar accuracy as prior state-of-the-art works on
depth estimation at a faster inference speed. Our proposed networks,
RT-MonoDepth and RT-MonoDepth-S, runs at 18.4\&30.5 FPS on NVIDIA Jetson Nano
and 253.0\&364.1 FPS on NVIDIA Jetson AGX Orin on a single RGB image of
resolution 640$\times$192, and achieve relative state-of-the-art accuracy on
the KITTI dataset. To the best of the authors' knowledge, this paper achieves
the best accuracy and fastest inference speed compared with existing fast
monocular depth estimation methods.