Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs

TL;DR

This paper introduces two GPU-optimized methods to accelerate Oriented FAST feature detection, significantly improving real-time SLAM performance on low-end embedded devices like Jetson TX2.

Contribution

It proposes novel GPU-specific acceleration techniques for Oriented FAST, enabling over 7x faster feature detection on embedded GPUs, enhancing real-time SLAM capabilities.

Findings

Over 7x speedup on Jetson TX2

Effective optimization of FAST and Harris detection steps

Potential for real-time SLAM in resource-constrained environments

Abstract

The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual reality, which primarily focuses on detecting feature points from visual images to construct an unknown environmental map and simultaneously determines its own location. It usually imposes stringent requirements on hardware power consumption, processing speed and accuracy. Currently, the ORB (Oriented FAST and Rotated BRIEF)-based SLAM systems have exhibited superior performance in terms of processing speed and robustness. However, they still fall short of meeting the demands for real-time processing on mobile platforms. This limitation is primarily due to the time-consuming Oriented FAST calculations accounting for approximately half of the entire SLAM system. This paper presents two methods to accelerate the Oriented FAST feature detection on low-end embedded GPUs. These methods optimize the most time-consuming steps in Oriented FAST feature detection: FAST feature point detection and Harris corner detection, which is achieved by implementing a binary-level encoding strategy to determine candidate points quickly and a separable Harris detection strategy with efficient low-level GPU hardware-specific instructions. Extensive experiments on a Jetson TX2 embedded GPU demonstrate an average speedup of over 7.3 times compared to widely used OpenCV with GPU support. This significant improvement highlights its effectiveness and potential for real-time applications in mobile and resource-constrained environments.