Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Design Example: Measuring Distance Between Two Points with Obstructions

Design Example: Measuring Distance Between Two Points with Obstructions

When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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...

Common Leveling Mistakes and Errors

Common Leveling Mistakes and Errors

A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Systematic analysis of the potential prebiotic effects of Blackberry Seed Polysaccharides by utilizing the in vitro spatio-temporal dynamic digestion models.

Food research international (Ottawa, Ont.)·2025

Same author

Spatiotemporal digestion and intestinal microbial fermentation properties of Ganoderma lucidum based on in vitro digestive system.

Food chemistry·2025

Same author

Tunable Bandgap and Optical Properties of Black Phosphorene Nanotubes.

Materials (Basel, Switzerland)·2018

Same author

Solid-phase microextraction coupled to LC-ESI-MS/MS: evaluation and correction for matrix-induced ionization suppression/enhancement for pharmaceutical analysis in biological and environmental samples.

Analytical chemistry·2011

Same author

Shape-persistent two-component 2D networks with atomic-size tunability.

Chemistry, an Asian journal·2011

Same author

The products of the thermal decomposition of CH3CHO.

The Journal of chemical physics·2011

Search research articles

Related Experiment Video

Updated: Jun 25, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

A Novel High-Precision Railway Obstacle Detection Algorithm Based on 3D LiDAR.

Zongliang Nan^1,2, Guoan Zhu^1,2, Xu Zhang³

¹Laboratory of All-Solid-State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This study introduces a precise 3D LiDAR obstacle detection algorithm for railway safety. The system achieves over 95% detection for 15cm obstacles, enhancing railway security.

Keywords:

LiDAR PCA SFRE local-ICP railway

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Related Experiment Videos

Last Updated: Jun 25, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Area of Science:

Robotics and Automation
Railway Engineering
Computer Vision

Background:

Ensuring railway safety requires robust obstacle detection systems.
Existing methods face challenges with point cloud errors and varying terrain.
Traditional fixed thresholds limit the accuracy of obstacle detection algorithms.

Purpose of the Study:

To develop a high-precision obstacle detection algorithm for railways using 3D mechanical LiDAR.
To improve point cloud accuracy and rail extraction in diverse railway environments.
To enhance the dynamic adaptability and classification capabilities of obstacle detection systems.

Main Methods:

A projection-based calibration method and a novel rail extraction algorithm were developed.
A modulation function based on directional density variations dynamically adjusts detection thresholds.
Principal Component Analysis (PCA) and local Iterative Closest Point (ICP) were used for feature analysis and classification.

Main Results:

The system achieved a stable detection rate (STDR) over 95% for 15cm x 15cm x 15cm obstacles within ±25m.
An STDR exceeding 80% was recorded for 10cm x 10cm x 10cm obstacles within ±20m.
The algorithm effectively handled terrain variations and preserved track point cloud characteristics.

Conclusions:

The proposed 3D LiDAR-based algorithm offers a viable solution for railway obstacle detection.
The novel calibration, rail extraction, and dynamic thresholding methods significantly improve detection accuracy.
This research contributes to enhanced railway safety through advanced obstacle detection technology.