4 accepted papers
We present Graphite, a GPU-accelerated nonlinear least squares graph optimization framework. It provides a CUDA C++ interface to enable the sharing of code between a real-time application, such as a SLAM system, and its optimization tasks. The framework supports techniques to reduce memory usage, in…
The tracking module of a visual-inertial SLAM system processes incoming image frames and IMU data to estimate the position of the frame in relation to the map. It is important for the tracking to complete in a timely manner for each frame to avoid poor localization or tracking loss. We therefore pre
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 repeat…
In this paper, we present our approach to efficiently leveraging GPU resources to improve the performance of local bundle adjustment for visual-inertial SLAM. We observe that for local bundle adjustment (i) the Schur complement method, a technique often used to speed up bundle adjustment, has the la…