Luthardt, Stefan ; Han, Chao ; Willert, Volker ; Schreier, Matthias (2019)
Efficient Graph-Based V2V Free Space Fusion.
2017 IEEE Intelligent Vehicles Symposium (IV). Redondo Beach, CA, USA (11.06. -14.06.2017)
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
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Kurzbeschreibung (Abstract)
A necessary prerequisite for future driver assistance systems as well as automated driving is a suitable and accurate representation of the environment around the vehicle with a sufficient range. To extend the range of the environment representation, sharing the environment detections of multiple vehicles via vehicle-to-vehicle (V2V) communication is a promising approach. In this paper, we present a method to fuse shared free space detections from multiple vehicles. The detections are represented as Parametric Free Space (PFS) maps, which are especially suitable for real-time radio V2V-transmission due to their compactness.
A graph-based algorithm to fuse PFS maps is proposed that solves possible contradictions between the maps and incorporates the maps' uncertainty attributes. By solely operating on the contour, the fusion can be carried out by a simple path search in a fusion graph that is constructed from the maps. This results in an efficient method that finds the fusion result within few iterations.
To account for possible errors in the relative poses between the PFS maps, we furthermore present an adapted Iterative Closest Point (ICP) matching to align the maps before the fusion. Therein we employ a modified soft-assign scheme for robust outlier rejection, and incorporate the PFS maps' boundary orientation to improve the matching process. We show the capabilities of our method by presenting results on real test drive data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Luthardt, Stefan ; Han, Chao ; Willert, Volker ; Schreier, Matthias |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Efficient Graph-Based V2V Free Space Fusion |
| Sprache: | Englisch |
| Publikationsjahr: | 16 Januar 2019 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2017 |
| Verlag: | IEEE |
| Buchtitel: | IEEE Symposium on Intelligent Vehicle |
| Veranstaltungstitel: | 2017 IEEE Intelligent Vehicles Symposium (IV) |
| Veranstaltungsort: | Redondo Beach, CA, USA |
| Veranstaltungsdatum: | 11.06. -14.06.2017 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/8358 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | A necessary prerequisite for future driver assistance systems as well as automated driving is a suitable and accurate representation of the environment around the vehicle with a sufficient range. To extend the range of the environment representation, sharing the environment detections of multiple vehicles via vehicle-to-vehicle (V2V) communication is a promising approach. In this paper, we present a method to fuse shared free space detections from multiple vehicles. The detections are represented as Parametric Free Space (PFS) maps, which are especially suitable for real-time radio V2V-transmission due to their compactness. A graph-based algorithm to fuse PFS maps is proposed that solves possible contradictions between the maps and incorporates the maps' uncertainty attributes. By solely operating on the contour, the fusion can be carried out by a simple path search in a fusion graph that is constructed from the maps. This results in an efficient method that finds the fusion result within few iterations. To account for possible errors in the relative poses between the PFS maps, we furthermore present an adapted Iterative Closest Point (ICP) matching to align the maps before the fusion. Therein we employ a modified soft-assign scheme for robust outlier rejection, and incorporate the PFS maps' boundary orientation to improve the matching process. We show the capabilities of our method by presenting results on real test drive data. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-83584 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme) |
| Hinterlegungsdatum: | 20 Jun 2024 16:26 |
| Letzte Änderung: | 20 Jun 2024 16:26 |
| PPN: | 442892624 |
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| Suche nach Titel in: | TUfind oder in Google |
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