Towards Fast and Automatic Map Initialization for Monocular SLAM Systems

Abstract

Simultaneous localization and mapping (SLAM) is a widely adopted approach for estimating the pose of a sensor with 6 degrees of freedom. SLAM works by using sensor measurements to initialize and build a virtual map of the environment, while simultaneously matching succeeding sensor measurements to entries in the map to perform robust pose estimation of the sensor on each measurement cycle. Markerless, single-camera systems that utilize SLAM usually involve initializing the map by applying one of a few structure-from-motion approaches to two frames taken by the system at different points in time. However, knowing when the feature matches between two frames will yield enough disparity, parallax, and/or structure for a good initialization to take place remains an open problem. To make this determination, we train a number of logistic regression models on summarized correspondence data for 927 stereo image pairs. Our results show that these models classify with significantly higher precision than the current state-of-the-art approach in addition to remaining computationally inexpensive.