Improving RGB-D SLAM in dynamic environments: A motion removal approach
By Yuxiang Sun, Ming Liu, Max Qing-Hu Meng. Robotics Auton. Syst., 2017
350 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/9c6399da218db35896473240baa88225bca0a8b1
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
- Yuxiang Sun
- Ming Liu
- José Neira
- Javier Civera
- Gwenolé Corre
- Frédérick Carrel
- Andréa Macario Barros
- Maugan Michel
In Computer Vision and Pattern Recognition
- Yuxiang Sun
- H. Wang
- Ming Liu
- José Neira
- Javier Civera
- Gwenolé Corre
- Frédérick Carrel
- Andréa Macario Barros
Citations in the corpus
- A Comprehensive Survey of Visual SLAM Algorithms (2022, background): “The literature presents many different visual-SLAM algorithms that make researchers’ choices difficult, without criteria, when it comes to evaluating their benefits and drawbacks.”
- Self-Supervised Drivable Area and Road Anomaly Segmentation Using RGB-D Data For Robotic Wheelchairs (2019, background): “RGB-D cameras, such as Kinect [1], are visual sensors that can stream RGB and depth images at the same time [2]–[4].”
- DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments (2018, methodology): “Compared with previous non real-time methods to filter out dynamic objects, such as [19], DS-SLAM is more satisfied with the needs of the real time.”
- DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes (2018, background): “[16] calculate the difference in intensity between consecutive RGB images.”