INDOOR NAVIGATION BY USING SEGMENTATION OF RANGE
IMAGES OBTAINED BY LASER SCANNERS
B. Gorte a , K. Khoshelham a , E. Verbree b
a Optical and Laser Remote Sensing, Delft University of Technology, The Netherlands
b GIS Technology, Delft University of Technology, The Netherlands
{b.g.h.gorte, k.khoshelham, e.verbree}@tudelft.nl
Commission I, ICWG-I/V
KEY WORDS: laser scanning, localization, mapping, navigation, robotics, segmentation, tracking
ABSTRACT:
This paper proposes a method to reconstruct the trajectory of a moving laser scanner on the basis of indoor scans made at different
positions. The basic idea is that a position with respect to a measured scene is completely determined by the distances to a minimum
of three intersecting planes within that scene, and therefore displacements are defined by changes in those distances between
successive scans. A segmentation algorithm based on the gradients of a range image is used to extract planar features from the
measured scene. The correspondence establishment between planes of two successive scans is formulated as a combinatorial
problem, and a heuristic search method is employed to find combinations that indicate corresponding sets of planes between the two
scans. The position of the scanner at each scan is computed by intersecting sets of four planes. An experiment with multiple scans
obtained by a Faro laser scanner in an indoor environment is described, and the results show the reliability and accuracy of the
proposed indoor navigation technique.
1. INTRODUCTION
Terrestrial laser scanning is a popular method for acquiring 3D
data of the interior of buildings and closed environments such
as tunnels. Laser range data can also be used for positioning
and navigation through sensing features that have known
coordinates. The combination of the above tasks, mapping and
navigation, is known in the field of robotics as simultaneous
localization and mapping (SLAM). The problem of
simultaneous localization and mapping concerns the
measurement of an environment using a sensor such as a laser
scanner, while these measurements are processed to create a
(3D) map of the environment and at the same time determine
the position of the sensor with respect to the obtained map.
Example applications of simultaneous 3D modelling and
localization include autonomous vehicles,' intelligent
wheelchairs and automated data acquisition within indoor
environments.
In recent years, simultaneous localization and mapping in both
outdoor and indoor environments has been well researched by
the scientists in the field of robotics and automation. In indoor
environments, localization methods based on a 2D laser range
finder have been more popular (Zhang and Ghosh, 2000; Xu et
al., 2003; Victorino and Rives, 2004), but these methods
provide only a 2D map of the environment. Other sensors that
have been used for localization and mapping include sonar
(Leonard and Durrant-Whyte, 1991) and cameras with
structured light (Kim and Cho, 2006) as well as unstructured
light (Diebel et al., 2004). Some of the existing approaches
utilize multiple sensors and adopt a sensor fusion strategy.
Dedieu et al., (2000) combine a camera and a 2D laser scanner.
Newman et al., (2002) and Araneda et al., (2007) opt for the
combination of a laser scanner and an odometer. Diosi and
Kleeman (2004) fuse laser range data with data acquired from a
sonar. Guivant et al., (2000) utilize a laser scanner with a
number of beacons for the localization, although natural
landmarks are also taken into account to improve the
localization accuracy.
In this paper, we describe a method for simultaneous
localization and 3D modelling of an indoor environment using
a laser scanner. The proposed method is based on extracting
planar features from the range images, and positioning the laser
scanner with respect to corresponding planes in successive
scans. The localization is, therefore, entirely based on the
measured scene, and no beacons or known landmarks are
required. In addition, the mapping is carried out in 3D since the
laser scanner provides a 3D point cloud of the scanned scene.
The paper is structured in five sections. Section 2 describes the
algorithm for the segmentation of the range images into planar
features. In Section 3, the method for sensor localization using
the extracted planar features is discussed. Experimental results
of a test with multiple scans in an indoor environment are
presented in Section 4. Conclusions appear in Section 5.
2. RANGE IMAGE SEGMENTATION AND
EXTRACTION OF PLANAR FEATURES
The fundamental data structure of terrestrial laser scans is the
range image (rather than the point cloud), since it is most
closely linked to the scanner’s operation, measurement of the
range (distance between the scanner and a point) as function of
an azimuth (horizontal) and a zenith (vertical) angle. The range
image is a discrete representation of a point cloud in a spherical
coordinate system. With the scanner at the centre of the sphere,
the azimuth and zenith angles determine the columns and row
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