OpenVSLAM: A Versatile Visual SLAM Framework

Abstract

In this paper, we introduce OpenVSLAM, a visual SLAM framework with high usability and extensibility. Visual SLAM systems are essential for AR devices, autonomous control of robots and drones, etc. However, conventional open-source visual SLAM frameworks are not appropriately designed as libraries called from third-party programs. To overcome this situation, we have developed a novel visual SLAM framework. This software is designed to be easily used and extended. It incorporates several useful features and functions for research and development.

References in corpus (4)

• ORB-SLAM: a Versatile and Accurate Monocular SLAM System
• ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
• Faster and better: a machine learning approach to corner detection
• ProSLAM: Graph SLAM from a Programmer's Perspective

Cited by in corpus (25)

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• 3D Scene Geometry Estimation from 360${}^{\circ }$ Imagery: A Survey
• A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
• Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
• SLAM for Visually Impaired People: a Survey
• UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision
• OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision
• Sensor Fusion for Autonomous Indoor UAV Navigation in Confined Spaces
• Deployment of Aerial Robots after a major fire of an industrial hall with hazardous substances, a report
• OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds
• Building Movie Map -- A Tool for Exploring Areas in a City -- and its Evaluation
• OpenHPS: An Open Source Hybrid Positioning System
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• Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data
• Loosely coupled 4D-Radar-Inertial Odometry for Ground Robots
• Privacy Preserving Visual SLAM
• SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment
• Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction
• AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments
• Video-Based Camera Localization Using Anchor View Detection and Recursive 3D Reconstruction
• Hierarchical Segment-based Optimization for SLAM
• A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis