Occupancy grid computation from dense stereo and sparse structure and motion points for automotive applications

Abstract

We present a complete processing chain for computing 2D occupancy grids from image sequences. A multi layer grid is introduced which serves several purposes. First the 3D points reconstructed from the images are distributed onto the underlying grid. Thereafter a virtual measurement is computed for each cell thus reducing computational complexity and rejecting potential outliers. Subsequently a height profile is updated from which the current measurement is partitioned into ground and obstacle pixels. Different height profile update strategies are tested and compared yielding a stable height profile estimation. Lastly the occupancy layer of the grid is updated. To asses the algorithm we evaluate it quantitatively by comparing the output of it to ground truth data illustrating its accuracy. We show the applicability of the algorithm by using both, dense stereo reconstructed and sparse structure and motion points. The algorithm was implemented and run online on one of our test vehicles in real time.