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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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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Real-Time 2-D Lidar Odometry Based on ICP.

Fuxing Li1, Shenglan Liu1, Xuedong Zhao1

  • 1College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a 2-D lidar odometry system using an Iterative Closest Point (ICP) variant for real-time, low-drift performance. The method enhances feature extraction and scan matching, outperforming existing SLAM algorithms in accuracy.

Keywords:
2-D lidarfeature extractionmotion estimationmulti-scale

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Accurate real-time localization is crucial for autonomous systems.
  • Existing Simultaneous Localization and Mapping (SLAM) algorithms face challenges in drift and computational efficiency.

Purpose of the Study:

  • To develop a robust and efficient 2-D lidar odometry system.
  • To improve scan-matching speed and accuracy through multi-scale feature extraction.
  • To achieve low-drift performance in real-time applications.

Main Methods:

  • A 2-D lidar odometry system based on a variant of the Iterative Closest Point (ICP) algorithm.
  • Multi-scale feature extraction classifying point clouds by curvature (smooth and rough).
  • Extraction of edge and corner points, utilizing tangent-vector-pairs for motion estimation.

Main Results:

  • The proposed method demonstrates real-time and low-drift performance.
  • Achieved robust feature extraction and accelerated scan-matching.
  • Outperformed Cartographer and Hector SLAM in accuracy on indoor datasets.

Conclusions:

  • The developed lidar odometry system offers a significant improvement in accuracy and efficiency.
  • The multi-scale feature extraction approach enhances robustness for lidar-based localization.
  • This method provides a competitive alternative for real-time SLAM applications.