Robust monocular SLAM in dynamic environments

Tan, Wei; Liu, Haomin; Dong, Zilong; Zhang, Guofeng; Bao, Hujun

doi:10.1109/ismar.2013.6671781

Cited by 128 publications

(31 citation statements)

References 43 publications

(62 reference statements)

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“…The solutions to the SLAM problem in dynamic scenes are roughly divided into two categories according to the object, the first of which is to operate on features and reduce their influence on the estimation of pose [12]- [15]. Most SLAM systems treat dynamic points as outliers and are not involved in localization and mapping.…”

Section: Related Workmentioning

confidence: 99%

Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

Liao

Huang

et al. 2021

IEEE Access

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The accuracy of localization and mapping of automated guided vehicles (AGVs) using visual simultaneous localization and mapping (SLAM) is significantly reduced in a dynamic environment compared to a static environment due to incorrect data association caused by dynamic objects. To solve this problem, a robust stereo SLAM algorithm based on dynamic region rejection is proposed. The algorithm first detects dynamic feature points from the fundamental matrix of consecutive frames and then divides the current frame into superpixels and labels its boundaries with disparity. Finally, dynamic regions are obtained from dynamic feature points and superpixel boundaries types; only the static area is used to estimate the pose to improve the localization accuracy and robustness of the algorithm. Experiments show that the proposed algorithm outperforms ORB-SLAM2 in the KITTI dataset, and the absolute trajectory error in the actual dynamic environment can be reduced by 84% compared with the conventional ORB-SLAM2, which can effectively improve the localization and mapping accuracy of AGVs in dynamic environments.INDEX TERMS SLAM, dynamic area detection, stereo vision, automatic guided vehicle.

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Section: Related Workmentioning

confidence: 99%

Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

Liao

Huang

et al. 2021

IEEE Access

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“…2a). The webcam and HMD are connected to a PC running monocular SLAM (Simultaneous Localization and Mapping) software to visually track the scene from the live video [49]. The SLAM software is highly reliable (95% success with 1,000 iterations) and provides robust tracking even when the scene is changed, for example when the user rearranges books on a desk.…”

Section: Basic Annotation Systemmentioning

confidence: 99%

Using Freeze Frame and Visual Notifications in an Annotation Drawing Interface for Remote Collaboration

Kim¹,

Billinghurst²,

Lee³

et al. 2018

KSII TIIS

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This paper describes two user studies in remote collaboration between two users with a video conferencing system where a remote user can draw annotations on the live video of the local user's workspace. In these two studies, the local user had the control of the view when sharing the first-person view, but our interfaces provided instant control of the shared view to the remote users. The first study investigates methods for assisting drawing annotations. The auto-freeze method, a novel solution for drawing annotations, is compared to a prior solution (manual freeze method) and a baseline (non-freeze) condition. Results show that both local and remote users preferred the auto-freeze method, which is easy to use and allows users to quickly draw annotations. The manual-freeze method supported precise drawing, but was less preferred because of the need for manual input. The second study explores visual notification for better local user awareness. We propose two designs: the red-box and both-freeze notifications, and compare these to the baseline, no notification condition. Users preferred the less obtrusive red-box notification that improved awareness of when annotations were made by remote users, and had a significantly lower level of interruption compared to the both-freeze condition.

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“…SLAM is a principal technique that is used to progressively construct a map within an unprepared environment, while its current locaton is tracked based on the extracted scene features [38]. This approach has been applied for the robust tracking of the AR in a small space.…”

Section: Vision-based Approachesmentioning

confidence: 99%

Augmented-Reality Survey: from Concept to Application

2017

KSII TIIS

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The recent advances in the field of augmented reality (AR) have shown that the technology is a fundamental part of modern immersive interactive systems for the achievement of user engagement and a dynamic user experience. This survey paper presents the descriptions of a variety of the new AR explorations, and the issues that are relevant to the contemporary development of the fundamental technologies and applications are discussed. Most of the literature regarding the pertinent topics-taxonomy, the core tracking and sensing technologies, the hardware and software platforms, and the domain-specific applications-are then chronologically surveyed, and in varying detail, this is supplemented with the cited papers. This paper portrays the diversity of the research regarding the AR field together with an overview of the benefits and the limitations of the competing and complementary technologies.

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Robust monocular SLAM in dynamic environments

Cited by 128 publications

References 43 publications

Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

Using Freeze Frame and Visual Notifications in an Annotation Drawing Interface for Remote Collaboration

Augmented-Reality Survey: from Concept to Application

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