Local exploration: online algorithms and a probabilistic framework

Mapping an environment with an imaging sensor becomes very challenging if the environment to be mapped is unknown and has to be explored. Exploration involves the planning of views so that the entire environment is covered. The majority of implemented mapping systems use a heuristic planning while theoretical approaches regard only the traveled distance as cost. However, practical range acquisition systems spend a considerable amount of time for acquisition. In this paper, we address the problem of minimizing the cost of looking around a corner, involving the time spent in traveling as well as the time spent for reconstruction. Such a local exploration can be used as a subroutine for global algorithms. We prove competitive ratios for two online algorithms. Then, we provide two representations of local exploration as a Markov Decision Process and apply a known policy iteration algorithm. Simulation results show that for some distributions the probabilistic approach outperforms deterministic strategies. Comments Copyright 2003 IEEE. Reprinted from Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA 2003), Volume 2, pages 1913-1920. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=27834&page=3 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/32 Proceedings of the 1003 IEEE loternational Conference on Robotics & Automation Taipei, Taiwan, September 14-19, 2003 Local Exploration: Online Algorithms and a Probabilistic Framework Volkan Mer, Sampath Kannan, and Kostas Daniilidis Depanment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, 19104 {isleri,kannan,kostas}@cis.upenn.edu AbstractMapping an environment with an imaging sensor becomes very challenging if the environment to be mapped is unknown and has to he explored. Exploration involves the planning of views so that the entire environment is covered. The majority of implemented mapping systems use a heuristic planning while theoretical approaches regard only the traveled distance as cost. However, practical range acquisition systems spend a considerable amount of time for acquisition. In this paper, we address the problem of minimizing the cost of looking around a comer, involving the time spent in traveling as well as the time spent for reconstruction. Such a local exploration can he used as a subroutine for global algorithms. We prove competitive ratios for two online algorithms. Then, we provide two representations of local exploration as a Markov Decision Process and apply a known policy iteration algorithm. Simulation results show that for some distributions the probabilistic approach outperforms deterministic strategies. Mapping an environment with an imaging sensor becomes very challenging if the environment to be mapped is unknown and has to he explored. Exploration involves the planning of views so that the entire environment is covered. The majority of implemented mapping systems use a heuristic planning while theoretical approaches regard only the traveled distance as cost. However, practical range acquisition systems spend a considerable amount of time for acquisition. In this paper, we address the problem of minimizing the cost of looking around a comer, involving the time spent in traveling as well as the time spent for reconstruction. Such a local exploration can he used as a subroutine for global algorithms. We prove competitive ratios for two online algorithms. Then, we provide two representations of local exploration as a Markov Decision Process and apply a known policy iteration algorithm. Simulation results show that for some distributions the probabilistic approach outperforms deterministic strategies.