Dynamic Control Information for the relative Positioning of Nodes in a Sensor Network
|Duration:||01.12.2016 – 01.12.2019|
|Funded by:||Deutsche Forschungsgemeinschaft (DFG)|
Autonomous driving comes with the need to handle highly dynamic environments. To ensure safe navigation and to enable the interaction with other traffic participants, 3D scene reconstruction and the identification and reconstruction of moving objects, especially of vehicles, are fundamental tasks.
Furthermore, collaborative vehicle positioning requires knowledge about the relative poses between cars for them to be used as vehicle-to-vehicle (V2V) measurements.
Enabling the communication and transmission of relative poses between the vehicles allows incorporating them as dynamic control information to enhance the positioning. This leads to the need of techniques for precise 3D object reconstruction to derive the poses of other vehicles relative to the position of the observing vehicle. In this context, stereo cameras provide a cost-effective solution for sensing a vehicle's surroundings.
Consequently, this project is mainly based on stereo images acquired by a stereo camera rig mounted on the moving vehicle as observations and has the goal to detect and identify other sensor nodes, i.e. other vehicles in this case, and to determine their relative poses.
Most of the existing techniques for vehicle detection and pose estimation are restricted to a coarse estimation of the viewpoint in 2D, whereas the precise determination of vehicle pose, especially of the orientation, and vehicle shape is still an open problem, that is addressed here.
The goal of this project is to propose a method for precise 3D reconstruction of vehicles in order to reason about the relative vehicle poses in 3D, i.e. the position and rotation of the vehicles with respect to the observing vehicle, and to leverage the determined shape for the identification of other sensor nodes.
For the detection of vehicles, we combine a generic 3D object detection approach with a state-of-the-art image object detector. To reason about the vehicle poses and shapes, a deformable vehicle model is estimated for every vehicle detection. The model fitting is based on reconstructed 3D points, image features and automatically derived scene knowledge. Fig. 1 shows exemplary results of estimated model wireframes backprojected to the image plane.
Fig. 1: Estimated vehicle models backprojected to the image.
Overall research project: “Integrity and Collaboration in Dynamic Sensor Networks” (i.c.sens)
This project is part of the international Research Training Group “i.c.sens” (https://www.icsens.uni-hannover.de). The aim of the Research Training Group is to investigate concepts for ensuring the integrity of collaborative systems in dynamic sensor networks.
Coenen, M.; Rottensteiner, F.; Heipke, C. (2017): Detection and 3D modelling of vehicles from mobile mapping stereo images In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1, pp. 505-512 more
Coenen, M.; Rottensteiner, F.; Heipke, C. (2018): Recovering the 3D pose and shape of vehicles from stereo images. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2, pp. 73-80. more