Stationary Detector for Monocular Visual-Inertial SLAM

Monocular Visual-Inertial SLAM (VISLAM) algorithms are very popular solutions for accurate indoor localization. However, they may suffer from speed divergence when the system is at rest as illustrated on Figure 1. In this paper we propose to tackle this issue. For that we investigate the detection of time epochs when a visual-inertial sensor rig is stationary. Two kind of stops are deduced from raw sensor data. SoftStop when the system is at rest with a slight movement noise (e.g a human at rest holding the system) and HardStop when the system is perfectly at rest (e.g a robot at rest holding the system). We propose an inertial detector and a visual detector to decide if the system is on move, on SoftStop or HardStop and describe how to take advantage of this additional information in a VISLAM. A significant accuracy gain and better robustness against divergence is demonstrated on our datasets.