Orb Slam2

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[Submitted on 3 Feb 2015 (v1), last revised 18 Sep 2015 (this version, v2)]

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

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本文总结了特征点法slam中目前效果最好的方法：orb-slam2 / orb-slam3 相关改进代码汇总，包括加速、多传感器融合、稠密建图、线特征、点线融合、导航、动态环境、多平台移植等。. ORB-SLAM2 ROS node This is the ROS implementation of the ORB-SLAM2 real-time SLAM library for Monocular, Stereo and RGB-D cameras that computes the camera trajectory and a sparse 3D reconstruction (in the stereo and RGB-D case with true scale). It is able to detect loops and relocalize the camera in real time. Abstract: In this paper, we present a case study which investigates whether/how Simultaneous Localization and Mapping (SLAM), e.g., the ORB-SLAM2 application, can be executed on a small, energy-efficient, multi-processor embedded platform with an ARM big.LITTLE architecture, e.g., the ODROID-XU4 platform, mounted on a small drone with a limited energy budget while meeting real-time performance.

From: Raul Mur-Artal [view email]
[v1] Tue, 3 Feb 2015 18:52:23 UTC (3,614 KB)
[v2]Orb slam2 paperFri, 18 Sep 2015 09:50:11 UTC (3,827 KB)
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Raul Mur-Artal
J. M. M. Montiel
Juan D. Tardós
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[Submitted on 20 Oct 2016 (this version), latest version 19 Jun 2017 (v2)]
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Abstract: We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-Dcameras, including map reuse, loop closing and relocalization capabilities. Thesystem works in real-time in standard CPUs in a wide variety of environmentsfrom small hand-held indoors sequences, to drones flying in industrialenvironments and cars driving around a city. Our backend based on BundleAdjustment with monocular and stereo observations allows for accuratetrajectory estimation with metric scale. Our system includes a lightweightlocalization mode that leverages visual odometry tracks for unmapped regionsand matches to map points that allow for zero-drift localization. Theevaluation in 29 popular public sequences shows that our method achievesstate-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, butwith the aim of being an out-of-the-box SLAM solution for researchers in otherfields.

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From: Raul Mur-Artal [view email]
Slam2[v1]Thu, 20 Oct 2016 16:04:31 UTC (4,005 KB)
[v2] Mon, 19 Jun 2017 04:44:33 UTC (4,033 KB)
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