JetVision: A Low-Cost Real-Time Depth Estimation System using Jetson Computing Platform
Siddhanta Mandal, Kartik E. Cholachgudda, Lohith V. Chamakura, Rajashekhar C. Biradar, Geetha D. Devanagavi · 2023
Stereo vision is an important technique in computer vision that enables depth information to be extracted from a pair of cameras. Depth estimation has numerous applications across various fields. Some of the notable applications include autonomous vehicles, augmented reality (AR) and virtual reality (VR), robotics, 3D reconstruction, surveillance and security and medical imaging. In this research, the authors develop and test a low-cost real-time stereo vision system, JetVision, that uses two Raspberry Pi cameras, a Jetson computing platform and CUDA programming. The algorithm developed for JetVision is designed to efficiently analyze the differences between the images captured by each camera to estimate the depth of objects in the scene using Semi-Global Matching (SGM) method. To achieve real-time performance, the algorithm is implemented using CUDA programming, which enables efficient parallel processing on Jetson’s GPU. The algorithm is tested on a dataset of real-world images captured by JetVision and is compared its result with high-grade Intel® RealSenseTMdepth camera and ground truth depth information. The results demonstrate that JetVision is capable of accurately estimating depth information in real-time with an FPS ranging between 15 to 28. This research contributes to the field of computer vision by demonstrating the potential of stereo vision algorithms in real-world applications using cost-effective hardware and efficient parallel processing.