A Low-Power Hardware Accelerator for ORB Feature Extraction in Self-Driving Cars

Abstract

Simultaneous Localization And Mapping (SLAM) is a key component for autonomous navigation. SLAM consists of building and creating a map of an unknown environment while keeping track of the exploring agent's location within it. An effective implementation of SLAM presents important challenges due to real-time inherent constraints and energy consumption. ORB-SLAM is a state-of-the-art Visual SLAM system based on cameras that can be used for self-driving cars. In this paper, we propose a high-performance, energy-efficient and functionally accurate hardware accelerator for ORB-SLAM, focusing on its most time-consuming stage: Oriented FAST and Rotated BRIEF (ORB) feature extraction. We identify the BRIEF descriptor generation as the main bottleneck, as it exhibits highly irregular access patterns to local on-chip memories, causing a high performance penalty due to bank conflicts. We propose a genetic algorithm to generate an optimal memory access pattern offline, which greatly simplifies the hardware while minimizing bank conflicts in the computation of the BRIEF descriptor. Compared with a CPU system, the accelerator achieves 8x speedup and 1957x reduction in power dissipation.