Abstract
Laser scanners are state-of-the-art devices used for
mapping in service, industry, medical and rescue
robotics. Although a lot of work has been done in
laser-based SLAM, maps still suffer from
interferences caused by objects like glass, mirrors
and shiny or translucent surfaces. Depending on the
surface's reflectivity, a laser beam is deflected
such that returned measurements provide wrong
distance data. At certain positions phantom-like
objects appear. This paper describes a specular
reflectance detection approach applicable to the
emerging technology of multi-echo laser scanners in
order to identify and filter reflective objects. Two
filter stages are implemented. The first filter
reduces errors in current scans on the fly. A second
filter evaluates a set of laser scans, triggered as
soon as a reflective surface has been passed. This
makes the reflective surface detection more robust
and is used to refine the registered
map. Experiments demonstrate the detection and
elimination of reflection errors. They show improved
localization and mapping in environments containing
mirrors and large glass fronts is improved.
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