eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform

TL;DR

This paper presents eSLAM, an FPGA-based energy-efficient accelerator that enables real-time ORB-SLAM processing with significant improvements in frame rate and energy efficiency compared to traditional CPUs.

Contribution

The paper introduces a novel FPGA architecture for ORB-SLAM that reformulates feature descriptors and employs optimizations for real-time, energy-efficient visual SLAM.

Findings

Up to 3X frame rate improvement over Intel i7

Up to 31X frame rate improvement over ARM Cortex-A9

Up to 71X energy efficiency improvement over Intel i7

Abstract

Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation. However, due to the computational complexity of SLAM algorithms, it is very difficult to achieve real-time implementation on low-power platforms.We propose an energy efficient architecture for real-time ORB (Oriented-FAST and Rotated- BRIEF) based visual SLAM system by accelerating the most time consuming stages of feature extraction and matching on FPGA platform.Moreover, the original ORB descriptor pattern is reformed as a rotational symmetric manner which is much more hardware friendly. Optimizations including rescheduling and parallelizing are further utilized to improve the throughput and reduce the memory footprint. Compared with Intel i7 and ARM Cortex-A9 CPUs on TUM dataset, our FPGA realization achieves up to 3X and 31X frame rate improvement, as well as up to 71X and 25X energy efficiency improvement, respectively.