Stationary Detector for Monocular Visual-Inertial SLAM

Richard Guillemard, Fraçois Hélénon, Bruno Petit, Vincent Gay-Bellile, Mathieu Carrier

Abstract:

Monocular Visual-Inertial SLAM (VISLAM) algo-
rithms 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.