Name
Abstract | ||
|---|---|---|
This work presents two different forms of dense, high-resolution radar data from two frequency modulated continuous wave radar sensors, along sparse radar pointclouds produced by one of the radar sensors. In addition, all datasets include 3D lidar and inertial measurements, and a lidar-based simultaneous localization and mapping pose estimation. Over 2 h of 6D pose data was generated across 52 datasets collected in highly diverse 3D environments including lab spaces, outside and inside large buildings, urban walkways, and a mine. One dataset, from the ASPEN Lab, also includes precision groundtruth generated from a motion capture system. Intrinsic radar calibration and measured extrinsic sensor position calibrations are also provided along with python based development tools to interact with the various datasets. This data is designed to assist with generating radar based localization algorithms and calibrations between radar and other sensors. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1177/02783649211068535 | INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH |
Keywords | DocType | Volume |
Dataset, radar, field robotics, SLAM, odometry, mapping | Journal | 41 |
Issue | ISSN | Citations |
4 | 0278-3649 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Kramer | 1 | 0 | 1.01 |
| Kyle Harlow | 2 | 0 | 0.34 |
| Christopher R. Williams | 3 | 0 | 1.01 |
| Christoffer R. Heckman | 4 | 12 | 10.78 |