FG-SLAM: A Hybrid Approach to Grid Based Mapping Enhanced by Geometric Features
Date Issued
2009
Date
2009
Author(s)
Kuo, Wei-Jen
Abstract
In this thesis, we present a novel data structure representing the environment with occupancy grid cells while each grid map is associated with a set of features extracted from laser scan points, call FG (Feature-Grid) mapping. Due to the fact that line segments are principal elements of a great variety of indoor environments, they provide considerable geometric information about the environment and hence which can be used for enhancing the localization accuracy. Orthogonal characteristic of line features is the key to guarantee consistency of the resulting SLAM algorithm since the lines we are dealing with are either parallel or perpendicular to one another. Besides this, corners are also features that provide crucial location information. These special surrounding features allow the later used particle filter to sample robot poses more correctly. As a result, in this work the large loop map building can be closed without actually incorporating any loop closing mechanism. Experimental results are carried out successfully in relatively large challenging indoor environments, which contain both loops and long featureless corridors, with robots equipped with SICK LMS-100 laser scanner whose maximum range is 20 meters.
Subjects
Simultaneous localization & mapping (SLAM)
Rao-Blackwellized particle filters (RBPFs)
loop closure
grid maps
feature-grid maps
Type
thesis
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