@inproceedings {pub3111pub3180,
	title = {Stereo Visual Odometry without Temporal Filtering},
	author = {J{\"o}rg Deigm{\"o}ller AND Julian Eggert},
	year = {2016},
	month = {September},
	abstract = {Visual Odometry is one of the key technology for navigating and percepting the environment of an autonomous vehicle. 
Within the last ten years, a common sense has been established how to implement a high precision and robust system.
This paper goes one step back by avoiding temporal filtering and relying only on pure measurements that have been 
carefully selected. The focus here is on estimating the ego-motion rather a detailed reconstruction of the scene. 
Different approaches for selecting proper 3D-flows (Scene Flows) are compared and discussed. The ego-motion is computed 
by a standard P8P-approach encapsulated in a RANSAC environmet. Finally, a method is proposed that is wihtin the top 
ranks of the KITTI benchmark.},
	publisher = {German Conference on Pattern Recognition},
	booktitle = {German Conference on Pattern Recognition}
}