Title: Obstacle Detection Based on Partial 3D Reconstruction

Authors: Zhongfei Zhang, Richard S. Weiss, and Allen R. Hanson

Affiliation: Computer Vision Research Laboratory, Dept. of 
Computer Science, University of Massachusetts, Box 34610, Amherst, MA
0l003-4610

Abstract:Three different algorithms for qualitative obstacle detection are
presented in this paper. Each one is based on different assumptions.
The first two algorithms are aimed at yes/no obstacle detection without
indicating which points are obstacles. They have the advantage of fast
determination of the existence of obstacles in a scene based on
the solvability of a linear system.
The first algorithm uses information about the
ground plane, while the second algorithm only assumes that the ground
is planar.
The third algorithm continuously estimates the ground plane, and based on that
determines the height of each matched point in the scene.
Experimental results are presented for real and simulated  data,
and performances of the three algorithms under different noise levels
are compared in simulation.
%The three algorithms make different assumptions about the environment.
We conclude that in terms of the robustness of performance, the third
one works best.

Keywords: obstacle detection, structure from motion, uncalibrated stereo,
Kalman filtering, mobile robotics, goal-oriented vision