ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

TL;DR

ORB-SLAM2 is a versatile, real-time SLAM system supporting monocular, stereo, and RGB-D cameras, offering accurate mapping, loop closing, relocalization, and map reuse across diverse environments, with publicly available source code.

Contribution

It introduces a comprehensive SLAM system that integrates map reuse, loop closing, relocalization, and supports multiple camera types, achieving state-of-the-art accuracy.

Findings

Achieves state-of-the-art accuracy on public sequences.

Operates in real-time on standard CPUs across various environments.

Provides open-source code for widespread use.

Abstract

We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches to map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.