RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910571918524416 |
|---|---|
| author | Hilger, Maximilian Mandischer, Nils Corves, Burkhard |
| author_facet | Hilger, Maximilian Mandischer, Nils Corves, Burkhard |
| contents | Rescue robotics sets high requirements to perception algorithms due to the unstructured and potentially vision-denied environments. Pivoting Frequency-Modulated Continuous Wave radars are an emerging sensing modality for SLAM in this kind of environment. However, the complex noise characteristics of radar SLAM makes, particularly indoor, applications computationally demanding and slow. In this work, we introduce a novel radar SLAM framework, RaNDT SLAM, that operates fast and generates accurate robot trajectories. The method is based on the Normal Distributions Transform augmented by radar intensity measures. Motion estimation is based on fusion of motion model, IMU data, and registration of the intensity-augmented Normal Distributions Transform. We evaluate RaNDT SLAM in a new benchmark dataset and the Oxford Radar RobotCar dataset. The new dataset contains indoor and outdoor environments besides multiple sensing modalities (LiDAR, radar, and IMU). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11576 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform Hilger, Maximilian Mandischer, Nils Corves, Burkhard Robotics Computer Vision and Pattern Recognition Signal Processing Rescue robotics sets high requirements to perception algorithms due to the unstructured and potentially vision-denied environments. Pivoting Frequency-Modulated Continuous Wave radars are an emerging sensing modality for SLAM in this kind of environment. However, the complex noise characteristics of radar SLAM makes, particularly indoor, applications computationally demanding and slow. In this work, we introduce a novel radar SLAM framework, RaNDT SLAM, that operates fast and generates accurate robot trajectories. The method is based on the Normal Distributions Transform augmented by radar intensity measures. Motion estimation is based on fusion of motion model, IMU data, and registration of the intensity-augmented Normal Distributions Transform. We evaluate RaNDT SLAM in a new benchmark dataset and the Oxford Radar RobotCar dataset. The new dataset contains indoor and outdoor environments besides multiple sensing modalities (LiDAR, radar, and IMU). |
| title | RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform |
| topic | Robotics Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2408.11576 |