Abstract
This paper proposes an algorithm for estimation of mobile robot motion. The geometry of surrounding space is described with range scans (samples of distance measurements) taken by the mobile robot’s range sensors. A similar sample of space geometry in any arbitrary preceding moment of time or the environment map can be used as a reference. The suggested algorithm is invariant to isotropic scaling of samples or map that allows using samples measured in different units and maps made at different scales. The algorithm is based on Hough transform: it maps from measurement space to a straight-line parameters space. In the straight-line parameters, space the problems of estimating rotation, scaling and translation are solved separately breaking down a problem of estimating mobile robot localization into three smaller independent problems. The specific feature of the algorithm presented is its robustness to noise and outliers inherited from Hough transform. The prototype of the system of mobile robot orientation is described.
Highlights
For simultaneous real time localization and mapping, two approaches are usually applied to estimate relative position of a mobile robot with measurements from the onboard sensors [1]: feature points-based method; samples mapping
The positions of feature points are defined in the coordinate plane of a mobile robot and its localization is defined from comparing feature points taken from two samples
The algorithm described in their studies has several drawbacks: its translation invariance is limited to small translation values; defining the rotation period demands computations in the measurement space; there is no description of the calculation method; the problem of scaling estimation is not considered
Summary
For simultaneous real time localization and mapping, two approaches are usually applied to estimate relative position of a mobile robot with measurements from the onboard sensors [1]: feature points-based method; samples mapping. The algorithm described in their studies has several drawbacks: its translation invariance is limited to small translation values; defining the rotation period demands computations in the measurement space; there is no description of the calculation method; the problem of scaling estimation is not considered. The algorithm for the estimation of rotation assumes that measurements of samples M1 and M2 are transformed to Hough Space [ , ] calculated by (1), the result of which are accumulators A1 and A2.
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