SVIn2: Sonar Visual-Inertial SLAM with Loop Closure for Underwater
  Navigation

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

This paper presents a novel tightly-coupled keyframe based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted to the underwater domain. The state-of-the-art visual-inertial state estimation package OKVIS has been significantly augmented to accommodate acoustic data from sonar and depth measurements from pressure sensor, along with visual and inertial data in a non-linear optimization-based framework. The main contributions of this paper are: a robust initialization method to refine scale using depth measurements and a real-time loop-closing and relocalization method. An additional contribution is the tightly-coupled optimization formulation using acoustic, visual, inertial, and depth data. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate the performance of our approach.

Related collections

Most cited references 7

Record: found
Abstract: found
Article: found

Is Open Access

ORB-SLAM: a Versatile and Accurate Monocular SLAM System

, , (2015)

This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.