ROSS: Radar Off-road Semantic Segmentation
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Abstract (arXiv)
As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR data for off-road scenarios. We present a novel pipeline that utilizes LIDAR data and an existing annotated off-road LIDAR dataset for generating RADAR labels, in which the RADAR data are represented as images. Validated with real-world datasets, our pragmatic approach underscores the potential of RADAR technology for navigation applications in off-road environments.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 20 Oct 2023.
Builds on
11 of 14 references.
References · 11
- 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
- 2021FAST-LIO2: Fast Direct LiDAR-inertial Odometrycited by 183
- 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 134
- 2020RELLIS-3D Dataset: Data, Benchmarks and Analysiscited by 119
- 2020Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentationcited by 112
- 2020FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filtercited by 74
- 2020RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radarcited by 22
- 2018Probably Unknown: Deep Inverse Sensor Modelling In Radarcited by 16
- 2019Radar-based Feature Design and Multiclass Classification for Road User Recognitioncited by 6
- 2019Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensemblescited by 6
- 2023Improving Extrinsics between RADAR and LIDAR using Learningcited by 2
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Citing papers per year: 2024: 1.
1 paper.