RatSLAM: a hippocampal model for simultaneous localization and mapping
By Michael Milford, Gordon Fraser Wyeth, D. Prasser. IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, 2004
436 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/eb1ded2a07fba8a4a9a3df1a650a91ce4880f066
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
- Davide Scaramuzza
- Roland Yves Siegwart
- Friedrich Fraundorfer
- Michael Milford
- Karen Simonyan
- Gordon Fraser Wyeth
- Jorge Fuentes-Pacheco
- Juan Manuel Rendón-Mancha
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Jitendra Malik
- Vladlen Koltun
- Trevor Darrell
- Xiaogang Wang
- Dacheng Tao
- Abhinav Kumar Gupta
Citations in the corpus
- Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC (2009, methodology): “a car, which was then used in a RatSLAM scheme [11] to generate a coherent map of the urban environment.”
- Visual simultaneous localization and mapping: a survey (2012, methodology): “6.3 Bio-inspired models Milford et al. (2004) use models of the hippocampus (responsible for spatial memory) of rodents to create a location and mapping system called RatSLAM.”
- Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook (2021, methodology): “The first such SLAM network, RatSLAM, was proposed by Milford et al. [120].”
- Persistent Navigation and Mapping using a Biologically Inspired SLAM System (2010, methodology): “The paper draws on earlier work published in conference papers (Milford et al. 2004, 2005, 2006), and which has been used in offline dataset SLAM experiments (Milford and Wyeth, 2008a, b).”
- SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights (2012, methodology): “The approach taken in this work is partly inspired by the author's previous work in vision-based mapping in RatSLAM [16] [17] [18].”
- Learning to Navigate in Cities Without a Map (2018, positive): “RatSLAM demonstrates localisation and path planning over long distances using a biologically-inspired architecture (Milford et al., 2004).”
- PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, background): “As a first-level classification, SLAM systems can be divided into topological (e.g. [2]–[5]) and metric approaches.”