5 papers
TurboMap: GPU-Accelerated Local Mapping for Visual SLAM
Parsa Hosseininejad, Kimia Khabiri, Shishir Gopinath +3
In real-time Visual SLAM systems, local mapping must operate under strict latency constraints, as delays degrade map quality and increase the risk of tracking failure. GPU parallel…
FastTrack: GPU-Accelerated Tracking for Visual SLAM
Kimia Khabiri, Parsa Hosseininejad, Shishir Gopinath +2
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…
FastLoop: Parallel Loop Closing with GPU-Acceleration in Visual SLAM
Soudabeh Mohammadhashemi, Shishir Gopinath, Kimia Khabiri +3
Visual SLAM systems combine visual tracking with global loop closure to maintain a consistent map and accurate localization. Loop closure is a computationally expensive process as…
SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditions
Mohamed Hefny, Karthik Dantu, Steven Y. Ko
We present SAL (SLAM Adversarial Lab), a modular framework for evaluating visual SLAM systems under adversarial conditions such as fog and rain. SAL represents each adversarial con…
Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework
Shishir Gopinath, Karthik Dantu, Steven Y. Ko
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 appl…