A Comprehensive Triangulation Framework for Mapping Applications
Abstract
Modern remote sensing platforms such as unmanned aerial vehicles (UAVs) that can carry a variety of sensors including RGB frame cameras, hyperspectral (HS) line cameras, and LiDAR sensors are commonly used in several application domains. In order to derive accurate products such as point clouds and orthophotos, sensors’ interior and exterior orientation parameters (IOP and EOP) must be established. These parameters are derived/refined in a triangulation framework through minimizing the discrepancy between conjugate features extracted from involved datasets. Existing triangulation approaches are not general enough to deal with varying nature of data from different sensors/platforms acquired in diverse environmental conditions. This research develops a generic triangulation framework that can handle different types of primitives (e.g., point, linear, and/or planar features), and sensing modalities (e.g., RGB cameras, HS cameras, and/or LiDAR sensors) for delivering accurate products under challenging conditions with a primary focus on digital agriculture and stockpile monitoring application domains.The developed framework in this research starts with a fully-automated triangulation strategy that relies on available UAV trajectory information for reducing point feature matching ambiguity in RGB images acquired over agricultural fields. Then, a multi-scale matching strategy is introduced for automated triangulation of frame/line camera images acquired at different flying heights. To assure a good quality of generated orthophotos from UAV images captured at low altitudes over tall plants, plant row segments are extracted/matched in imagery and used as linear features in the triangulation process. Finally, a linear feature-based triangulation of image/LiDAR data captured by proximal sensing systems with challenging viewing geometry is introduced for indoor stockpile monitoring. Experimental results from real datasets demonstrate the feasibility of the proposed multi-primitive, multi-modal triangulation framework in providing accurate IOP/EOP, and consequently, accurate points clouds/orthophotos.
Degree
Ph.D.
Advisors
Habib, Purdue University.
Subject Area
Agriculture|Remote sensing|Aerospace engineering|Optics|Robotics|Transportation
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