The diverging approach from the traditional planar (2-D) road data models is multi-dimensionality, involving 3-D geometry and time.
Evaluation both on the public KITTI benchmark and our own dataset show that this is a viable approach for visual odometry which outperforms basic 3D pose estimation due to the exploitation of the largely planar structure of road environments.
It uses a single CCD camera placed on a high pole in a corner of the LC, classifying objects such as cars, bikes, trucks, pedestrians, dogs, and papers and localizing them according to the camera calibration, assuming a planar model of the road and railroad.
In most urban scenes, planar surfaces (such as roads, parking lots, buildings, and corridors) are abundant especially in regions of human activity.
In our work, we have made the assumption that the road surface is planar.
The tracking condition involves the assumption that vehicles are moving on a planar surface (i.e., the road) with a locally constant velocity.
The network which they study exhibits a rather constant average degree over time, with only a very slight increase of the order of 0.2, this however can be due to the nature of the road network, which is planar, which means that it can be drawn on a sphere in such a way that no links will overlap or cross.
GPCR is based on the observation that the road network creates a naturally planar graph that can be exploited for communication purposes.
The spatial feature of farmland expansion is characterized by the radial expansion centered on the newly-built settlements, linear expansion along the roads, canals and rivers, and planar expansion in the irrigation districts.
Field work investigations have demonstrated that unplanned road excavations are producing mainly embankment shallow planar soil slides and/or gullying which primarily affect roads themselves, wiping them out and blocking vehicle circulation, and secondly, destroying or burying agriculture lands.
