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

In this paper, we introduce JacobiGPU, a tech- nique that uses a GPU to improve the efficiency of loop closure in visual-inertial SLAM systems, particularly when approxi- mating 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.