Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

66
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
66
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

421
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.
Here, in order to determine the magnitude of velocity and acceleration for point...
421
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.5K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.5K
Position and Displacement Vectors01:00

Position and Displacement Vectors

9.5K
To describe the motion of an object, one should first be able to describe its position (where it is at any particular time). More precisely, the position needs to be specified relative to a convenient frame of reference. A frame of reference is an arbitrary set of axes from which the position and motion of an object are described. Earth is often used as a frame of reference to describe the position of an object in relation to stationary objects on Earth.
Further, several important kinds of...
9.5K
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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

70
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...
70
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

486
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
486

You might also read

Related Articles

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

Sort by
Same author

Bacillus coagulans BC66 Attenuates Klebsiella pneumoniae-Induced Acute Lung Injury in Rabbits Associated With Modulation of Th17/Treg Immune Balance and Through the Keap1/Nrf2/HO-1 Signalling Pathway.

Probiotics and antimicrobial proteins·2026
Same author

Divergent roles of the Rag GTPases MoGtr1 and MoGtr2 in TOR signaling, autophagy, and pathogenicity of Magnaporthe oryzae.

Journal of genetics and genomics = Yi chuan xue bao·2026
Same author

TOP1α-mediated stabilization of CYP82C4 G-quadruplex DNA represses lateral root primordium initiation in Arabidopsis.

Nature plants·2026
Same author

A study on the association between tibial plateau fractures and intra-articular soft-tissue injuries under valgus injury mechanisms.

Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology·2026
Same author

Ethylene-glycol-assisted inkjet printing for controllable CVD growth of 2D MoS<sub>2</sub> single-crystal arrays.

Journal of colloid and interface science·2026
Same author

The association of nitrate exposure with bone mineral density in adolescents aged 12 to 19: A cross-sectional study.

Medicine·2026

Related Experiment Video

Updated: Jul 16, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.4K

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization.

Shuchen Xu1, Yongrong Sun1, Kedong Zhao1

  • 1National Key Laboratory of Helicopter Aeromechanics, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to improve vehicle positioning accuracy in urban areas by combining visual odometry with road network maps. The approach corrects accumulated errors, enhancing real-time location estimation.

Keywords:
map correction pointsoptimization and prediction modelpose graph optimizationroad network mapvisual odometry

More Related Videos

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

Related Experiment Videos

Last Updated: Jul 16, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.4K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Geographic Information Systems

Background:

  • Satellite navigation systems struggle with precision in urban environments due to signal obstruction.
  • Visual odometry offers an alternative but suffers from cumulative positioning errors inherent in dead-reckoning.

Purpose of the Study:

  • To develop a robust vehicle positioning method that overcomes the limitations of visual odometry in urban settings.
  • To enhance the accuracy and stability of real-time vehicle localization by integrating road network map information.

Main Methods:

  • A novel road-network-map-assisted positioning method utilizing pose graph optimization theory.
  • Integration of visual odometry dead-reckoning outputs with constraints derived from a point-line form road network map.
  • Development of an optimization and prediction model to correct visual odometry trajectories using map correction points.

Main Results:

  • The proposed method effectively suppresses accumulated positioning errors from visual odometry.
  • Experimental results on KITTI and campus datasets show superior performance compared to similar map-assisted techniques.
  • The method achieves stable and accurate real-time vehicle position estimation.

Conclusions:

  • Road network map-assisted visual odometry significantly improves vehicle positioning accuracy in challenging urban environments.
  • The pose graph optimization approach provides a reliable framework for correcting dead-reckoning drift.
  • This method offers a promising solution for precise and dependable autonomous vehicle navigation.