Cesar Rivadeneyra

Cornell University, Ithaca, New York, United States

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Publications (4)2.86 Total impact

  • César Rivadeneyra, Mark E. Campbell
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    ABSTRACT: Recent research has shown that robots can model their world with Multi-Level (ML) maps, which utilize patches in a two-dimensional grid space to represent various environment elevations within a given grid cell. Although these maps are able to produce three-dimensional models of the environment while exploiting the computational feasibility of single elevation maps, they do not take into account in-plane uncertainty when matching measurements to grid cells or when grouping those measurements into patches. To respond to these drawbacks, this paper proposes to extend these ML maps into Probabilistic Multi-Level (PML) maps, which use formal probability theory to incorporate estimation and modeling errors due to uncertainty. Measurements are probabilistically associated with cells near the nominal location, and are categorized through hypothesis testing into patches via classification methods that incorporate uncertainty. Experimental results on representative objects found in both indoor and outdoor environments show that PML generally outperforms ML, including in noisy and sparse data environments, by producing more consistent, informative and conservative maps. In addition, PML provides the framework to heterogeneous, cooperative mapping and a way to probabilistically discriminate between conflicting maps.
    The International Journal of Robotics Research 01/2011; 30:1508-1526. · 2.86 Impact Factor
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    ABSTRACT: Recent research has shown that robots can model their world with Multi-Level (ML) surface maps, which utilize dasiapatchespsila in a 2D grid space to represent various environment elevations within a given grid cell. Though these maps are able to produce 3D models of the environment while exploiting the computational feasibility of single elevation maps, they do not take into account in-plane uncertainty when matching measurements to grid cells or when grouping those measurements into dasiapatches.psila To respond to these drawbacks, this paper proposes to extend these ML surface maps into Probabilistic Multi-Level (PML) surface maps, which uses formal probability theory to incorporate estimation and modeling errors due to uncertainty. Measurements are probabilistically associated to cells near the nominal location, and are categorized through hypothesis testing into dasiapatchespsila via classification methods that incorporate uncertainty. Experimental results comparing the performances of the PML and ML surface mapping algorithms on representative objects found in both indoor and outdoor environments show that the PML algorithm outperforms the ML algorithm in most cases including in the presence of noisy and sparse measurements. The experimental results support the claim that the PML algorithm produces more densely populated, conservative representations of its environment with fewer measurements than the ML algorithm.
    Robotics and Automation, 2009. ICRA '09. IEEE International Conference on; 06/2009
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recent research has shown that robots can model their world with Multi-Level (ML) surface maps, which utilize 'patches' in a 2D grid space to represent various environment elevations within a given grid cell. Though these maps are able to produce 3D models of the environment while exploiting the computational feasibility of single elevation maps, they do not take into account in-plane uncertainty when matching measurements to grid cells or when grouping those measurements into 'patches.' To respond to these drawbacks, this paper proposes to extend these ML surface maps into Probabilistic Multi-Level (PML) surface maps, which uses formal probability theory to incorporate estimation and modeling errors due to uncertainty. Measurements are probabilistically associated to cells near the nominal location, and are categorized through hypothesis testing into 'patches' via classification methods that incorporate uncertainty. Experimental results comparing the performances of the PML and ML surface mapping algorithms on representative objects found in both indoor and outdoor environments show that the PML algorithm outperforms the ML algorithm in most cases including in the presence of noisy and sparse measurements. The experimental results support the claim that the PML algorithm produces more densely populated, conservative representations of its environment with fewer measurements than the ML algorithm.
    Proceedings of the 2009 IEEE international conference on Robotics and Automation; 05/2009
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    ABSTRACT: The Hydrothermal Vent BioSampler (HVB) currently being developed at the Jet Propulsion Laboratory is designed to collect large-volume samples of hydrothermal vent fluid, operating with fluid temperatures reaching 400°C and vent at depths of up to 6,500 meters. The primary goal of the project is to collect 'pristine' samples untainted by the surrounding waters. Analysis of the collected samples can reveal the existence of thermophillic organisms within the vent fluid, extending the upper limits of life with respect to thermo-tolerance. Any biology found at such environments can contribute to research in astrobiology, while the technology developed for the system can contribute to bio-containment techniques useful for Arctic and planetary exploration. The HVB performs in-situ filtering of hydrothermal vent fluids to concentrate a large volume of vent fluid to a smaller volume more suitable for transport. The HVB system is currently in the development phase. This paper provides a physical description of the current system, as well as a summary of the preliminary tests conducted in 2005: a pressure chamber test, a dive test in a 30 ft dive pool, and a dive operation at a hydrothermal vent off the northern coast of Iceland.

Publication Stats

9 Citations
2.86 Total Impact Points

Institutions

  • 2009–2011
    • Cornell University
      • Sibley School of Mechanical and Aerospace Engineering
      Ithaca, New York, United States