ORB SLAM accelerated on heterogeneous parallel architectures
SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end to end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB SLAM2. Through this, we attempt to implement the proposed optimization in FPGA based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high end GPU (NVIDIA GeForce 920MX) The implementation is achieved using high level synthesis based OpenCL for FPGA and CUDA for NVIDIA targeted boards.