OpenVSLAM: A Versatile Visual SLAM Framework

By Shinya Sumikura, Mikiya Shibuya, Ken Sakurada. ACM Multimedia, 2019

311 citations recorded by the source corpus.

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. OpenVSLAM is released at https://github.com/xdspacelab/openvslam under the 2-clause BSD license.

Subfields: Aerospace Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/e1d6452e00cb5f469e52425b49ef5238090af899

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Baofu Wu
2. Sidi Lu
3. Ren Zhong
4. Qingyan Zhang
5. Yongtao Yao
6. Weisong Shi
7. Liangkai Liu

In Ocean Engineering

1. Weisong Shi

Citations in the corpus

• Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, methodology): “Feature-based methods [109], [110], [111] extract features and track them to calculate the motion of the camera.”