Authors:
Sebastian Haner
and
Anders Heyden
Affiliation:
Lund University, Sweden
Keyword(s):
Path Planning, Next Best View Planning, Active Vision, Discrete Optimization, Semidefinite Programming, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
This paper presents a discrete model of a sensor path planning problem, with a long-term planning horizon.
The goal is to minimize the covariance of the reconstructed structures while meeting constraints on the length
of the traversed path of the sensor. The sensor is restricted to move on a graph representing a discrete set
of configurations, and additional constraints can be incorporated by altering the graph connectivity. This
combinatorial problem is formulated as an integer semi-definite program, the relaxation of which provides
both a lower bound on the objective cost and input to a proposed genetic algorithm for solving the original
problem. An evaluation on synthetic data indicates good performance.