Radar-Inertial Localization for Unmanned Ground Vehicles in GNSS-Denied Off-Road Environments

Radar is a growing candidate for localization in GNSS-denied scenarios that are challenging or restrictive to LiDAR. However, applications are still largely limited to indoor or urban environments. To bridge this gap, we look at the performance of radar-inertial-odometry in distinctly offroad environments that include a dense forest and underground mine. We develop a localization pipeline which uses an adaptive EKF for unstructured forested regions, and fuses both EKF estimates and point-to-plane radar ICP measurements via factor graph for structured underground environments. We observed high quality localization results using radar in these environments as well as improvements in accuracy when using our pipeline compared to current methods, particularly in the forested region where our AEKF was seen to be highly suitable for such terrains.

Authors

Publication Details

Journal
Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM)
Published
2024-10-09
DOI
https://doi.org/10.33012/2024.19697
Citations
1
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
1.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Radar-Inertial Localization for Unmanned Ground Vehicles in GNSS-Denied Off-Road Environments

Mohamed Atia, Petar Mitrev
1 citations
Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM)
Robotics and Sensor-Based Localization
1.30
article

Radar-Inertial Localization for Unmanned Ground Vehicles in GNSS-Denied Off-Road Environments

Mohamed Atia, Petar Mitrev
article en
1 citations

Abstract

Radar is a growing candidate for localization in GNSS-denied scenarios that are challenging or restrictive to LiDAR. However, applications are still largely limited to indoor or urban environments. To bridge this gap, we look at the performance of radar-inertial-odometry in distinctly offroad environments that include a dense forest and underground mine. We develop a localization pipeline which uses an adaptive EKF for unstructured forested regions, and fuses both EKF estimates and point-to-plane radar ICP measurements via factor graph for structured underground environments. We observed high quality localization results using radar in these environments as well as improvements in accuracy when using our pipeline compared to current methods, particularly in the forested region where our AEKF was seen to be highly suitable for such terrains.

Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM)
Sustainable cities and communities
Openalex Percentile: Top 12%
Robotics and Sensor-Based Localization
1.30
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.