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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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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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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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Types of Global Positioning System Surveys01:30

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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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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Centroid of a Body: Problem Solving01:03

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Related Experiment Video

Updated: Oct 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving

António Silva1, Duarte Fernandes1, Rafael Névoa1

  • 1Algoritmi Centre, University of Minho, 4800-058 Guimarães, Portugal.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study explores efficient deep learning for real-time object detection in autonomous vehicles using edge devices. It proposes a method to deploy powerful models on resource-constrained hardware, achieving comparable accuracy with lower power and space demands.

Keywords:
3D object detectionLiDAR scannersautonomous drivingdeep learning methodsonboard inferencequantisation methods

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Deep learning significantly advanced LiDAR object detection, primarily using powerful GPU servers for training and inference.
  • Autonomous driving faces limitations in onboard devices regarding space, power, and inference time compared to training hardware.

Purpose of the Study:

  • Investigate deep learning methods for power-effective, real-time object detection on edge devices for autonomous driving.
  • Develop a methodology to deploy high-end GPU models onto resource-constrained onboard systems.

Main Methods:

  • A two-stage approach: analyzing model hyperparameters for application requirements and network compression for resource limitations.
  • Utilized a hybrid FPGA-CPU board for onboard inference, evaluating performance on the KITTI dataset.

Main Results:

  • Achieved accuracy comparable to PC-based deep learning methods.
  • Demonstrated superior effectiveness for real-time inference in power-limited and space-constrained autonomous driving scenarios.

Conclusions:

  • The proposed methodology enables efficient deployment of deep learning models for onboard LiDAR object detection.
  • Hybrid FPGA-CPU solutions offer a viable, effective alternative for real-time inference in autonomous vehicles.