Matching Items (4)
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- All Subjects: Mapping
- Genre: Academic theses
- Creators: Das, Jnaneshwar
- Creators: DeVecchio, Duane
Description
Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree dimensions would speed up the measurement or data collection process and allow for a much larger set of trees to be used in studies. In turn, this would allow studies to make more generalizable inferences about areas with trees. To this end, this thesis focuses on developing a system that generates a semantic representation of the topology of a tree in real-time. The first part describes a simulation environment and a real-world sensor suite to develop and test the tree mapping pipeline proposed in this thesis. The second part presents details of the proposed tree mapping pipeline. Stage one of the mapping pipeline utilizes a deep learning network to detect woody and cylindrical portions of a tree like trunks and branches based on popular semantic segmentation networks. Stage two of the pipeline proposes an algorithm to separate the detected portions of a tree into individual trunk and branch segments. The third stage implements an optimization algorithm to represent each segment parametrically as a cylinder. The fourth stage formulates a multi-sensor factor graph to incrementally integrate and optimize the semantic tree map while also fusing two forms of odometry. Finally, results from all the stages of the tree mapping pipeline using simulation and real-world data are presented. With these implementations, this thesis provides an end-to-end system to estimate tree topology through semantic representations for forestry and precision agriculture applications.
ContributorsVishwanatha, Rakshith (Author) / Das, Jnaneshwar (Thesis advisor) / Martin, Roberta (Committee member) / Throop, Heather (Committee member) / Zhang, Wenlong (Committee member) / Ehsani, Reza (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work describes an approach for distance computation between agents in a
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
ContributorsMulford, Philip (Author) / Das, Jnaneshwar (Thesis advisor) / Takahashi, Timothy (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
Rock traits (grain size, shape, orientation) are fundamental indicators of geologic processes including geomorphology and active tectonics. Fault zone evolution, fault slip rates, and earthquake timing are informed by examinations of discontinuities in the displacements of the Earth surface at fault scarps. Fault scarps indicate the structure of fault zones fans, relay ramps, and double faults, as well as the surface process response to the deformation and can thus indicate the activity of the fault zone and its potential hazard. “Rocky” fault scarps are unusual because they share characteristics of bedrock and alluvial fault scarps. The Volcanic Tablelands in Bishop, CA offer a natural laboratory with an array of rocky fault scarps. Machine learning mask-Region Convolutional Neural Network segments an orthophoto to identify individual particles along a specific rocky fault scarp. The resulting rock traits for thousands of particles along the scarp are used to develop conceptual models for rocky scarp geomorphology and evolution. In addition to rocky scarp classification, these tools may be useful in many sedimentary and volcanological applications for particle mapping and characterization.
ContributorsScott, Tyler (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Committee member) / DeVecchio, Duane (Committee member) / Arizona State University (Publisher)
Created2020
Description
This study focuses on mapping faults along the Creeping Section of the San Andreas Fault (CSAF) in California between San Juan Bautista (121.54°W 36.85°N) and Parkfield (120.41°W 35.87°N). I synthesize high-quality base data, including and lidar topography from B4, EarthScope, and USGS 3DEP, recent maps of decadal-scale along-fault shear strain, and aerial and satellite imagery. Using these data, I produced (covering 150 km at 1:10,000 scale) three geospatial map datasets with attributes: geomorphic indicators of faulting, surficial geology, and active fault traces.The CSAF's creeping movement, though likely not associated with large earthquakes, has the potential to cause damage to infrastructure. Accurate fault mapping facilitates fault displacement hazard assessment. This type of work is useful for California state regulations, particularly the Alquist-Priolo Act of 1972, providing insights for engineering site assessments and fault exclusion zones.
I discern, categorize, and rank geomorphic indicators to support fault line placement. This approach contributes to the identification of surface expression of creeping faults where the surface has undergone alteration in response to displacement along the fault. I created a surficial geologic map spanning from San Juan Bautista to the southern extent of EarthScope lidar coverage (120.59°W 36.03°N). I categorized each fault as either a primary or secondary fault trace and further broke them into confidence levels based on interpretations of indicators along with structural geologic reasoning and
topographic patterns. Accessible target areas containing initial low confidence mapping or interesting structures were visited in the field.
Zones along the creeping section exhibit structures such as a pressure ridge found 25 km north of Parkfield, sigmoidal faults and sagponds observed near Paicines Ranch (121.29°W 36.68°N), en-echelon faults, horsetail splays and Riedel shear structures near Lewis Creek (120.87°W 36.29°N). Controls on the structural style along the CSAF are the results of geologic units through which the faults cut and fault zone width and trend.
ContributorsPowell, Joseph Hoss (Author) / Arrowsmith, Ramon (Thesis advisor) / Scott, Chelsea (Thesis advisor) / DeVecchio, Duane (Committee member) / DeLong, Stephen (Committee member) / Arizona State University (Publisher)
Created2023