JacobiGPU: GPU-Accelerated Numerical Differentiation for Loop Closure in Visual SLAM

Abstract

In this paper, we introduce JacobiGPU, a technique that uses a GPU to improve the efficiency of loop closure in visual-inertial SLAM systems, particularly when approximating Jacobians using the Finite Difference Method (FDM). Traditional FDM techniques often face computational overhead due to repeated perturbations in pose graphs. We address this overhead with a novel methodology, leveraging strategic graph partitioning and an optimized approach to Jacobian approximation. By integrating JacobiGPU into ORB-SLAM3’s g2o, we enhance the linearization process. Our evaluation, conducted on 12 sequences of varying lengths from the EuRoC and TUM-VI datasets, demonstrated a speedup of up to 4.23x in the linearization stage and an overall enhancement of up to 2.08x in the overall optimization process.