Learning and Evaluating Visual Features for Pose Estimation

Authors:
Robert Sim;Gregory Dudek
Affiliations:
-;-
Venue:
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Year:
1999

Citing 0
Cited 12

On the Automated Construction of Image-Based Maps

Autonomous Robots
CAD-Vision-Range-Based Self-Localization for Mobile Robot Using One Landmark

Journal of Intelligent and Robotic Systems
General Purpose Matching of Grey Level Arbitrary Images

IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Characterizing image sets using formal concept analysis

EURASIP Journal on Applied Signal Processing
Map building through pseudo dense scan matching using visual sonar data

Autonomous Robots
Grid-Based Visual SLAM in Complex Environments

Journal of Intelligent and Robotic Systems
WiSARD and NSP for Robot Global Localization

IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Efficient data association for view based SLAM using connected dominating sets

Robotics and Autonomous Systems
A neurosymbolic hybrid approach for landmark recognition and robot localization

BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Augmented reality system for visualizing 3-D region of interest in unknown environment

ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Bubble space and place representation in topological maps

International Journal of Robotics Research
Accelerated robust point cloud registration in natural environments through positive and unlabeled learning

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a method for learning a set of visual landmarks which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for different approaches to computing a pose estimate. Initially, each landmark is detected as a local extreme of a measure of distinctiveness and represented by a principal components encoding which is exploited for matching. Attributes of the observed landmarks can be parameterized using a generic parameterization method and then evaluated in terms of their utility for pose estimation. We present experimental evidence that demonstrates the utility of the method.