Fast Vision-guided Mobile Robot Navigation Using Model-based Reasoning And Prediction Of Uncertainties
By A. Kosaka, A. Kak. 1992
Subfields: Computer Vision and Pattern Recognition, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Li Fei-Fei
- Luc Van Gool
- Jitendra Malik
- Xiaogang Wang
- Ming-Hsuan Yang
- Trevor Darrell
- Dacheng Tao
- Cordelia Schmid
In Reinforcement Learning
- Benjamin Kuipers
- Guilherme N. DeSouza
- Yann LeCun
- Francisco Bonin-Font
- Michaël Mathieu
- Gabriel Oliver
- Alberto Ortiz
- Camille Couprie
Citations in the corpus
- Deep multi-scale video prediction beyond mean square error (2015, methodology): “Beside unsupervised learning, a video predictive system may find applications in robotics (Kosaka & Kak, 1992), video compression (Ascenso et al., 2005) and inpainting (Flynn et al., 2015) to name a few.”
- The Spatial Semantic Hierarchy (2000, methodology): “Other methods have been developed for visual sensing robots [26, 44].”
- Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST (2010, background): “In the remote sensing and GIS domains, studies have also explored the effectiveness of utilizing 2D drawings or GIS databases for improving the speed and accuracy of object recognition algorithms [50,52,84,90].”
- Vision for Mobile Robot Navigation: A Survey (2002, methodology): “As shown in [75], this is best done in Hough space where each straight line feature can be represented by a single point.”
- Visual Navigation for Mobile Robots: A Survey (2008, methodology): “FINALE [69] self-localizes using a geometrical representation of the environment and a Gaussian model for location uncertainty.”