PhD Thesis Poppinga: RRT - Jacobs University

Introduction
all of the few detected 3D points in each image set with all points in the next.
Due to limitations
in computing power at that time this took very long. It was also very error prone. Moravec
assumes an even floor and performs path-planning in 2D. The structure from motion approach in
[Charnley and Blissett, 1989] uses a corner detector which produces relatively sparse features. Based
on these, the authors evaluate the flatness of the terrain as seen from an autonomous vehicle. However,
they consider their inquiry in this direction a first tentative step, to cite:
“ It is clear that we do not drive using Structure-from-Motion alone and we are not
proposing that an autonomous vehicle should do so either. ”
Yet they do manage to extract point clouds and even to calculate the vehicle pose offline by matching
features in consecutive point clouds. By this, they contribute to making their caution a bit less justified
than it was at the time.
One of the first robotic systems with fully working 3D obstacle avoidance used binocular stereo
[Matthies et al., 1995]. They assume a horizontal ground plane, though not a fixed altitude. By
scanning columns in the disparity image, they detect deviation which they classify as obstacles.
[Simmons et al., 1996] uses a similar approach. They also assume a horizontal ground plane, but at a
fixed height. From the points derived from the stereo camera, they generate a local elevation map that
is used for obstacle avoidance. A drastically different approach is pursued in [Ohya et al., 1998]. It is
aimed at office environments where many horizontal and vertical lines can be detected in camera images
and where the assumption of a horizontal ground holds everywhere. Detected lines are matched
to the expected lines given by an a priori wire-frame model of the environment for self-localization.
Obstacle avoidance is done in 2D.
Stereo-vision has remained popular [Chao et al., 2009, Haddad et al., 1998, Schäfer et al., 2005a,
Schäfer et al., 2005b, Okada et al., 2001, Sabe et al., 2004, Larson et al., 2006], but other 3D sensing
methods have gained in use in obstacle avoidance: time-of-flight range cameras [Poppinga et al., 2008a,
Mihailidis et al., 2007] (light or laser based), vision augmented with 2D laser range-finders (LRF)
[Michels et al., 2005], object tracking [Michel et al., 2008], and most prominently LRF. In obstacle
avoidance, actuated ones are used [Kelly et al., 2006, Schafer et al., 2008], although in this case adequate
scanning speed is a challenge [Wulf and Wagner, 2003]. When LRFs are fixed in a position
not parallel to all driving directions, they are either inclined [Thrun et al., 2006] or perpendicular
to the floor. The latter case is found in 3D mapping [Howard et al., 2004, Thrun et al., 2000,
Hähnel et al., 2003], but is not useful for obstacle avoidance, since they do not look ahead.
The full scope of the three-dimensional data is not used by most approaches. Typically, they
assume a horizontal ground plane [Chao et al., 2009, Haddad et al., 1998] and enter the originally
3D data into a 2D map. Often the ground floor assumption is used not only to organize data, but
also to identify obstacles (which are defined as too big deviations from it) [Matthies et al., 1995,
Matthies et al., 2002, Simmons et al., 1996, Schäfer et al., 2005b].
This simplification works in man-made environments, but fails once robots have to operate in an
unstructured environment. In this case, one can identify terrain without searching for a ground plane
at all [Schäfer et al., 2005a]. There are, however, a few approaches that do try to find a ground plane
[Kelly et al., 2006, Simmons et al., 1996].
Recently, stereo-vision has been combined with monocular image classification for greater reliability
[Hadsell et al., 2009]. It also combines the longer range of the latter with the 3D map-building
potential of the former. Regions in the monocular image are classified using a neural network and the
ground plane is identified with a post-processed Hough transform. The results are combined into a
local 2D map.
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