An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics
By Khalid Yousif, Alireza Bab-Hadiashar, Reza Hoseinnezhad. 2015
308 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/bbd7b3646f2a6e15b1e903b9e80e2984b3dc5b9e
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
- Niki Trigoni
- Gwenolé Corre
- Frédérick Carrel
- Andréa Macario Barros
- Sei Ikeda
- Andrew Markham
- Takafumi Taketomi
- Maugan Michel
In Computer Vision and Pattern Recognition
- Niki Trigoni
- Gwenolé Corre
- Frédérick Carrel
- Andréa Macario Barros
- Sei Ikeda
- Andrew Markham
- Takafumi Taketomi
- Maugan Michel
Citations in the corpus
- Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, unknown): “Traditionally, VO was performed through two main approaches: feature-based approaches, where features such as lines or corners are detected, and appearance-based approaches, in which pixel intensity values are considered instead [150].”
- Visual SLAM algorithms: a survey from 2010 to 2016 (2017, background): “Camera based odometry called Visual Odometry (VO) is also one of the active research fields in the literature [16, 17].”
- A Comprehensive Survey of Visual SLAM Algorithms (2022, background): “The main difference between visual-SLAM and VO lies in considering, or not, the global consistency of the estimated trajectory and map [14].”
- Visual SLAM and Structure from Motion in Dynamic Environments (2018, background): “Yousif et al. (2015) [181] surveyed general visual SLAM approaches covering Visual Odometry (VO) and Visual SLAM, including filter, nonfilter, and RGB-D-based solutions.”