Visual-Inertial Monocular SLAM with Map Reuse

TL;DR

This paper introduces a visual-inertial SLAM system that can close loops and reuse maps to achieve zero-drift localization, addressing limitations of previous odometry methods, especially with monocular cameras.

Contribution

A novel tightly-coupled visual-inertial SLAM system that incorporates map reuse and a fast IMU initialization for monocular cameras, enabling drift-free localization.

Findings

Achieves 1% scale factor error in micro-aerial vehicle datasets.

Demonstrates superior accuracy over state-of-the-art odometry in revisiting sequences.

Successfully performs zero-drift localization with map reuse.

Abstract

In recent years there have been excellent results in Visual-Inertial Odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However these approaches lack the capability to close loops, and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this work we present a novel tightly-coupled Visual-Inertial Simultaneous Localization and Mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.