Assessment of SLAM Methods Applied in Monochromatic Environments

One of the most significant challenges in sustainable autonomous mobile robot and vehicle development is the perception of stochastic environments. Various environmental perception methods have been proposed to address these challenges; however, these methods often lack general applicability. Many of these methods rely on environmental feature extraction, which can fail in specific scenarios, such as monochromatic environments. This article aims to evaluate existing SLAM (Simultaneous Localization and Mapping) methods that utilize camera or combined camera and LiDAR input data in predominantly monochromatic environments. Additionally, this study seeks to identify performance issues in such applications.

Authors

Institutions

Publication Details

Published
2024-11-06
DOI
https://doi.org/10.3390/engproc2024079051
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessment of SLAM Methods Applied in Monochromatic Environments

Rudolf Krecht, Áron Ballagi
Robotics and Sensor-Based Localization
article

Assessment of SLAM Methods Applied in Monochromatic Environments

Rudolf Krecht, Áron Ballagi
article en

Abstract

One of the most significant challenges in sustainable autonomous mobile robot and vehicle development is the perception of stochastic environments. Various environmental perception methods have been proposed to address these challenges; however, these methods often lack general applicability. Many of these methods rely on environmental feature extraction, which can fail in specific scenarios, such as monochromatic environments. This article aims to evaluate existing SLAM (Simultaneous Localization and Mapping) methods that utilize camera or combined camera and LiDAR input data in predominantly monochromatic environments. Additionally, this study seeks to identify performance issues in such applications.

Széchenyi István University (HU)
Széchenyi István Egyetem
Life in Land
Openalex Percentile: Top 69%
Robotics and Sensor-Based Localization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.