Solving the Orienteering Problem Through Branch-And-Cut
INFORMS Journal on Computing
Automatic extraction of Irregular Network digital terrain models
SIGGRAPH '79 Proceedings of the 6th annual conference on Computer graphics and interactive techniques
Approximation Algorithms for Orienteering and Discounted-Reward TSP
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A Recursive Greedy Algorithm for Walks in Directed Graphs
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Improved algorithms for orienteering and related problems
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
The Euclidean Orienteering Problem Revisited
SIAM Journal on Computing
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using location based social networks for quality-aware participatory data transfer
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
A privacy-aware framework for participatory sensing
ACM SIGKDD Explorations Newsletter
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We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. We have proposed to implement PTD in two steps [10]. At the first step, termed viewpoint selection, a minimum number of points in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we focus on the viewpoint assignment problem. We first prove that this problem is an NP-hard problem, and therefore, the optimal solution for viewpoint assignment fails to scale as the extent of the urban environment and the number of participating users grow. Subsequently, we propose a family of heuristics for efficient viewpoint assignment to reduce the assignment running time while ensuring an almost complete texture collection. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.