Details
| Original language | English |
|---|---|
| Pages (from-to) | 21-28 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 4 |
| Issue number | 2/W5 |
| Publication status | Published - 29 May 2019 |
| Event | 4th ISPRS Geospatial Week 2019 - Enschede, Netherlands Duration: 10 Jun 2019 → 14 Jun 2019 |
Abstract
Interactive motion planing and collaborative positioning will play a key role in future autonomous driving applications. For this purpose, the precise reconstruction and pose estimation of other traffic participants, especially of other vehicles, is a fundamental task and will be tackled in this paper based on street level stereo images obtained from a moving vehicle. We learn a shape prior, consisting of vehicle geometry and appearance features, and we fit a vehicle model to initially detected vehicles. This is achieved by minimising an energy function, jointly incorporating 3D and 2D information to infer the model's optimal and precise pose parameters. For evaluation we use the object detection and orientation benchmark of the KITTI dataset (Geiger et al., 2012). We can show a significant benefit of each of the individual energy terms of the overall objective function. We achieve good results with up to 94.8% correct and precise pose estimations with an average absolute error smaller than 3° for the orientation and 33 cm for position.
Keywords
- 3D modelling, 3D reconstruction, autonomous driving, Object detection, pose estimation
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4, No. 2/W5, 29.05.2019, p. 21-28.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Precise vehicle reconstruction for autonomous driving applications
AU - Coenen, M.
AU - Rottensteiner, F.
AU - Heipke, C.
N1 - Funding Information: This work was supported by the German Research Foundation (DFG) as a part of the Research Training Group i.c.sens [GRK2159].
PY - 2019/5/29
Y1 - 2019/5/29
N2 - Interactive motion planing and collaborative positioning will play a key role in future autonomous driving applications. For this purpose, the precise reconstruction and pose estimation of other traffic participants, especially of other vehicles, is a fundamental task and will be tackled in this paper based on street level stereo images obtained from a moving vehicle. We learn a shape prior, consisting of vehicle geometry and appearance features, and we fit a vehicle model to initially detected vehicles. This is achieved by minimising an energy function, jointly incorporating 3D and 2D information to infer the model's optimal and precise pose parameters. For evaluation we use the object detection and orientation benchmark of the KITTI dataset (Geiger et al., 2012). We can show a significant benefit of each of the individual energy terms of the overall objective function. We achieve good results with up to 94.8% correct and precise pose estimations with an average absolute error smaller than 3° for the orientation and 33 cm for position.
AB - Interactive motion planing and collaborative positioning will play a key role in future autonomous driving applications. For this purpose, the precise reconstruction and pose estimation of other traffic participants, especially of other vehicles, is a fundamental task and will be tackled in this paper based on street level stereo images obtained from a moving vehicle. We learn a shape prior, consisting of vehicle geometry and appearance features, and we fit a vehicle model to initially detected vehicles. This is achieved by minimising an energy function, jointly incorporating 3D and 2D information to infer the model's optimal and precise pose parameters. For evaluation we use the object detection and orientation benchmark of the KITTI dataset (Geiger et al., 2012). We can show a significant benefit of each of the individual energy terms of the overall objective function. We achieve good results with up to 94.8% correct and precise pose estimations with an average absolute error smaller than 3° for the orientation and 33 cm for position.
KW - 3D modelling
KW - 3D reconstruction
KW - autonomous driving
KW - Object detection
KW - pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85067477570&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-IV-2-W5-21-2019
DO - 10.5194/isprs-annals-IV-2-W5-21-2019
M3 - Conference article
AN - SCOPUS:85067477570
VL - 4
SP - 21
EP - 28
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 2/W5
T2 - 4th ISPRS Geospatial Week 2019
Y2 - 10 June 2019 through 14 June 2019
ER -