SlimSLAM: An Adaptive Runtime for Visual-Inertial Simultaneous Localization and Mapping

Abstract

Simultaneous localization and mapping (SLAM) algorithms track an agent's movements through an unknown environment. SLAM must be fast and accurate to avoid adverse effects such as motion sickness in AR/VR headsets and navigation errors in autonomous robots and drones. However, accurate SLAM is computationally expensive and target platforms are often highly constrained. Therefore, to maintain real-time functionality, designers must either pay a large up-front cost to design specialized accelerators or reduce the algorithm's functionality, resulting in poor pose estimation.