Kalman Filter for Robot Vision: A Survey
By Shengyong Chen. IEEE transactions on industrial electronics (1982. Print), 2012
582 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/dd853fe190cbdc8741c679b5e7e43ef74f0b6ef9
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
In Computer Vision and Pattern Recognition
- Stefan Wermter
- Cornelius Weber
- Peter Johanns
- Timm Haucke
- Volker Steinhage
- François Auger
- Shen Yin
- Xiangping Zhu
In Electrical and Electronic Engineering
- François Auger
- T. Orłowska-Kowalska
- Shen Yin
- Stefan Wermter
- Cornelius Weber
- Xiangping Zhu
- Volker Steinhage
- Josep M. Guerrero
Citations in the corpus
- Industrial Applications of the Kalman Filter: A Review (2013, background): “An in-depth survey on the use of Kalman filters for vision based mobile robots is presented in [100].”
- A predictive network architecture for a robust and smooth robot docking behavior (2012, methodology): “A well-known method for tracking and prediction is the Kalman filter [21].”
- Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System (2015, background): “It plays an important role in many areas, such as process monitoring [1]–[3], target tracking [4], [5], and robot navigation [6], [7].”
- Visual Tracking: an Experimental Survey (2013, unknown): “Other surveys address robot applications where tracking based on a Kalman filter is well suited [19].”
- Multimodal sensory fusion for soccer robot self-localization based on long short-term memory recurrent neural network (2017, unknown): “Depending on the complex work environments and various robot sensors, there are various approaches in sensor fusion system applied for robot self-localization (Kam et al. 1997; Fox et al. 2003; Thrun 2002; Smith and Singh 2006; Aguirre et al. 2016; Chen 2012; Luo and Chang 2012).”
- Automated distance estimation for wildlife camera trapping (2022, background): “SORT connects these animal detections over all frames to cohesive tracks based on a Kalman-Filter framework (Chen, 2012) and the association metric of two visual detections by Intersection over Union (IoU) (Jaccard, 1912).”