CSCA-based expectivity indices for LIDAR computer vision

Authors:
Donald J. McTavish;Galina Okouneva;Aradhana Choudhuri
Affiliations:
Department of Aerospace Engineering, Ryerson University, Toronto, Ontario, Canada;Department of Aerospace Engineering, Ryerson University, Toronto, Ontario, Canada;Department of Aerospace Engineering, Ryerson University, Toronto, Ontario, Canada
Venue:
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Year:
2009

Citing 6
Cited 1

A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images

Image and Vision Computing - Special issue: range image understanding
Model Based Pose Estimation for Autonomous Operations in Space

ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Fast and accurate shape-based registration

Fast and accurate shape-based registration
Intelligent LIDAR scanning region selection for satellite pose estimation

Computer Vision and Image Understanding
A New Approach to Geometrical Feature Assessment for ICP-Based Pose Measurement: Continuum Shape Constraint Analysis

IMVIP '07 Proceedings of the International Machine Vision and Image Processing Conference

Near-optimal selection of views and surface regions for ICP pose estimation

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Continuum-Shape Constraint Analysis (CSCA) is a shape analysis approach applicable to pose estimation tasks in computer vision. A variety of useful measures (indices) which predict the accuracy of pose estimation can be derived from CSCA. Conceived for computer-vision assisted spacecraft rendezvous analysis, the approach was developed for blanket or localized scanning by LIDAR or similar range-finding scanner that samples non-specific points from the object across the area observed from a single view. The application problem addressed in this paper is the question of what view of an object can be expected to lead to the lowest pose estimation error computed via the Iterative Closest-Point Algorithm (ICP), or conversely, what level of error can be expected for a particular scan view. Based on CSCA, different forms of indices are developed for this purpose and demonstrated in both numerical and experimental studies using the Stanford Bunny and a cuboid shape. The continuum nature of the CSCA formulation produces metrics, including the Expectivity Index, that are pure shape properties an object.