Search research articles

Related Concept Videos

Errors in Global Positioning System

Errors in Global Positioning System

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,...

You might also read

Related Articles

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

Sort by

Same author

High-Resolution Wind Tunnel Dataset of Gas Sensor Responses to Vapor Plumes in Scale Model Landscapes.

Scientific data·2026

Same author

Video WeAther RecoGnition (VARG): An Intensity-Labeled Video Weather Recognition Dataset.

Journal of imaging·2024

Same author

Exploration and Gas Source Localization in Advection-Diffusion Processes with Potential-Field-Controlled Robotic Swarms.

Sensors (Basel, Switzerland)·2023

Same author

Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors.

Sensors (Basel, Switzerland)·2023

Same author

Fuzzy Cluster-Based Group-Wise Point Set Registration With Quality Assessment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023

Same author

A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization.

Sensors (Basel, Switzerland)·2022

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

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

Evaluating LiDAR Perception Algorithms for All-Weather Autonomy.

Himanshu Gupta¹, Achim J Lilienthal^1,2, Henrik Andreasson¹

¹Centre for Applied Autonomous Sensor Systems, Örebro University, 70182 Örebro, Sweden.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

Adverse weather degrades LiDAR performance in autonomous vehicles. While point cloud filtering helps, fine-tuning is crucial for effective noise reduction and reliable perception in all conditions.

Keywords:

3D object detection LiDAR perception SLAM adverse weather localization point cloud filter

More Related Videos

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

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

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

Published on: January 20, 2023

Related Experiment Videos

Last Updated: Jan 7, 2026

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

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

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

Published on: January 20, 2023

Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

LiDAR is essential for autonomous driving tasks like navigation and obstacle avoidance.
Adverse weather (snow, rain, fog) introduces noise into LiDAR data, impacting perception system reliability and safety.

Purpose of the Study:

Investigate LiDAR perception algorithm limitations in adverse weather.
Explore noise mitigation techniques for LiDAR data.
Propose future research for all-weather autonomous driving.

Main Methods:

Characterized LiDAR noise in snow, rain, and fog using real-world and synthetic datasets.
Evaluated point cloud filtering methods for denoising (processing time, accuracy, limitations).
Assessed the impact of weather and filtering on 3D object detection, localization, and SLAM.

Main Results:

Point cloud filtering partially reduces noise but requires specific tuning.
Adverse weather negatively impacts 3D object detection; dynamic filtering improves performance.
Localization is robust in snowfall but fails in dense fog; SLAM performs well in snowfall but fails in fog.

Conclusions:

LiDAR perception is challenged by adverse weather, necessitating adaptive filtering strategies.
Fine-tuning filtering methods is critical for specific LiDAR sensors, scenarios, and weather types.
Further research is needed to achieve robust all-weather LiDAR autonomy.