Holder, Martin Friedrich ; Hellwig, Sven ; Winner, Hermann (2019)
Real-Time Pose Graph SLAM based on Radar.
2019 IEEE Intelligent Vehicles Symposium (IV). Paris, France (09.06.2019-12.06.2019)
Conference or Workshop Item, Secondary publication, Postprint
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| Item Type: | Conference or Workshop Item |
|---|---|
| Type of entry: | Secondary publication |
| Title: | Real-Time Pose Graph SLAM based on Radar |
| Language: | English |
| Date: | 21 November 2019 |
| Place of Publication: | Darmstadt |
| Year of primary publication: | 2019 |
| Publisher: | IEEE |
| Book Title: | 2019 IEEE Intelligent Vehicles Symposium (IV) |
| Event Title: | 2019 IEEE Intelligent Vehicles Symposium (IV) |
| Event Location: | Paris, France |
| Event Dates: | 09.06.2019-12.06.2019 |
| Corresponding Links: | |
| Abstract: | This work presents a real-time pose graph based Simultaneous Localization and Mapping (SLAM) system for automotive Radar. The algorithm constructs a map from Radar detections using the Iterative Closest Point (ICP) method to match consecutive scans obtained from a single, front-facing Radar sensor. The algorithm is evaluated on a range of real-world datasets and shows mean translational errors as low as 0.62 m and demonstrates robustness on long tracks. Using a single Radar, our proposed system achieves state-of-the-art performance when compared to other Radar-based SLAM algorithms that use multiple, higher-resolution Radars. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-92856 |
| Additional Information: | See https://tuprints.ulb.tu-darmstadt.de/8756/ for video attachment |
| Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
| Divisions: | 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) > Driver Assistance |
| Date Deposited: | 21 Nov 2019 13:02 |
| Last Modified: | 03 Jul 2024 12:28 |
| URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/9285 |
| PPN: | 456541071 |
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