Real time System Implementation for Stereo 3D Mapping and Visual Odometry

Depth Estimation and localisation is a critical and computation heavy task. Due to its computation complexity, it is hard to achieve the high rate performance on the normal CPU. We here have as purpose compact, low power architecture for real time stereo depth estimation and stereo visual odometry that can be easily used on the UAV (unmanned aerial vehicle) and other autonomous navigation vehicles. A novel implementation of Stereo odometry based on careful feature selection and tracking [2] on GPU is described. It accelerates core computations like feature tracking, RANSAC based non linear solver using the GPU. 3D Mapping using stereo disparity estimation based on More Global Matching (MGM) a variant of SGM is implemented on FPGA. A Pipeline Architecture is introduced to increase throughput of the 3D map by leveraging multiple ARM cores. The programs are tested on Jetson Nano (an embedded GPU) and Ultra96 (ARM-FPGA Soc) which have less form factor and consume less power. Update rate of 20 is achieved for 6 degrees of freedom pose on Jetson Nano (60 on i7 core with Nvidia 1050ti) and an update rate of 16 fps is achieved for 3D point cloud on Ultra96 making the system desirable for above mentioned applications.