An 197-μJ/Frame Single-Frame Bundle Adjustment Hardware Accelerator for Mobile Visual Odometry

Abstract

This article presents an energy-efficient hardware accelerator for optimized bundle adjustment (BA) for mobile high-frame-rate visual odometry (VO). BA uses graph optimization techniques to optimize poses and landmarks and the applications are robot navigation, virtual reality (VR), and augmented reality (AR). Existing software implementations of BA optimization involve complex computational flows, numerical calculations, Lie group, and Lie algebra conversions. This poses challenges of slow computational speeds and high power consumption. A two-level reuse hardware architecture is proposed and implemented that efficiently updates the Jacobian matrix while reducing the field-programmable gate array (FPGA) hardware resources by 25%. A set of methodologies is proposed to quantify the errors caused by fixed-point systems during optimization. A fully pipelined architecture is implemented to increase computational speed while reducing hardware resources by 29%. This design features a parallel equation solver that improves processing speed by $2\times $ compared to conventional approaches. This article employs a single-frame local BA VO on the KITTI dataset and EuRoC dataset, achieving an average translational error of 0.75% and a rotational error of $0.0028~^{\circ } $ /m. The proposed hardware achieves a performance ranging from 188 to 345 frames/s in optimizing two main feature extraction methods with a maximum of 512 extracted feature points. Compared to state-of-the-art implementations, the accelerator achieved a minimum energy efficiency ratio of 11.6 mJ and $191~\mu $ J on the FPGA platform and application-specific integrated circuits (ASICs) platform, respectively. These improvements underscore the potential of FPGAs to enhance VO systems’ adaptability and efficiency in complex environments.