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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Field Application of Global Positioning System01:28

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Un SLAM dinámico de segmentación de movimiento para entornos interiores sin GNSS

Yunhao Wu1, Ziyao Zhang2,3, Haifeng Chen1

  • 1College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.

Sensors (Basel, Switzerland)
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Resumen

OS-SLAM mejora la localización y mapeo simultáneos (SLAM) en entornos dinámicos mediante el uso de segmentación de movimiento de flujo óptico. Este sistema robusto reduce significativamente los errores en las configuraciones negadas por GPS.

Palabras clave:
Entornos sin GNSSEscenario dinámicoflujo ópticoSegmentación semánticalocalización y cartografía simultáneas

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Área de la Ciencia:

  • La robótica
  • Visión por computadora
  • Inteligencia artificial

Sus antecedentes:

  • La localización y el mapeo simultáneos (SLAM) son cruciales en entornos sin GPS.
  • Los objetos y las variables dinámicas tienen un impacto negativo en la precisión de posición del SLAM.
  • La creación de mapas de elementos estáticos en escenas dinámicas es un desafío clave.

Objetivo del estudio:

  • Desarrollar un sistema SLAM robusto para entornos dinámicos.
  • Mejorar la precisión de posición y la coherencia de los mapas en entornos privados de GNSS.
  • Para reducir el impacto de los objetos dinámicos en el SLAM visual.

Principales métodos:

  • Se introdujo OS-SLAM, que integra la segmentación de movimiento de flujo óptico.
  • Desarrolló una ligera red de flujo óptico de múltiples escalas para una segmentación precisa del movimiento.
  • Propuso un enfoque de fusión YOLO-más rápido y Rigidmask para manejar objetos no rígidos.
  • Se generan mapas estáticos de nubes de puntos densos mediante el filtrado de nubes de puntos anormales.

Principales resultados:

  • OS-SLAM demostró una mayor robustez en entornos dinámicos.
  • El sistema redujo significativamente el impacto de los objetos dinámicos en la localización.
  • Los resultados experimentales mostraron reducciones sustanciales en el error de posición absoluta (APE) y el error de posición relativa (RPE) en comparación con ORB-SLAM3 en el conjunto de datos TUM.

Conclusiones:

  • OS-SLAM integra efectivamente el flujo óptico y la segmentación de movimiento para SLAM robusto.
  • El método de fusión propuesto mitiga los errores de segmentación causados por objetos dinámicos.
  • OS-SLAM ofrece un rendimiento superior en tareas SLAM visuales dinámicas, especialmente en escenarios difíciles sin GNSS.