Visual SLAM and Structure from Motion in Dynamic Environments
By Muhamad Risqi U. Saputra, Andrew Markham, Niki Trigoni. ACM Computing Surveys, 2018
407 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/cabecdfab3dcae6ecb70b1507acbf2a484c40552
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
- Luca Carlone
- Andrew Markham
- José A. Castellanos
- Julio A. Placed
- Vadim Indelman
- Henry Carrillo
- Agathoniki Trigoni
- Jared Strader
In Computer Vision and Pattern Recognition
- Andrew Markham
- Agathoniki Trigoni
- Yasin Almalioglu
- José A. Castellanos
- Luca Carlone
- Julio A. Placed
- Vadim Indelman
- Henry Carrillo
Citations in the corpus
- A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “The study of dynamic environments has long been a topic of interest for the path planning [257] and the SLAM [258] communities; but its investigation in the context of active SLAM has been typically restricted to the action execution step (i.”
- Distilling Knowledge From a Deep Pose Regressor Network (2019, methodology): “In particular, we employ DNN-based VO methods [33, 34, 37, 38, 26, 1] which replaces the conventional VO pipeline based on multiple-view geometry [15, 27] to DNN-based pipeline which automatically learns useful features for estimating 6 Degree-of-Freedom (DoF) camera poses.”