Techniques for evaluating optical flow for visual odometry in extreme terrain
By Jason Campbell, Rahul Sukthankar, Illah Reza Nourbakhsh. 2004
95 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
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
- Kurt Konolige
- Motilal Agrawal
- João Carreira
- Andrew Zisserman
- Carl Doersch
- Matthew A. Turk
- Oriol Vinyals
- Tobias Höllerer
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Jitendra Malik
- Dacheng Tao
- Trevor Darrell
- Andrea Vedaldi
- Cordelia Schmid
- Ming-Hsuan Yang
Citations in the corpus
- Perceiver IO: A General Architecture for Structured Inputs & Outputs (2021, background): “This has many broader applications, such as navigation and visual odometry in robots [11], estimation of 3D geometry [66], and even to aid transfer of more complex, learned inference such as 3D human pose estimation from synthetic to real images [23].”
- Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking (2011, background): “However, estimating movement from optical flow is prone to long-term drift, as the motion estimates and therefore the errors in the estimates are integrated over time, and is only feasible for smooth motion (DiVerdi and Höllerer 2008; Campbell et al. 2004).”
- Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS (2006, background): “[3] presents a visual odometry system that uses optical flow and a planar world assumption to obtain relative poses from a monocular camera.”