Abstract
Speeded Up Robust Feature (SURF) is used to position a robot with respect to an environment and aid in vision-based robotic navigation. During the course of navigation irregularities in the terrain, especially in an outdoor environment may deviate a robot from the track. Another reason for deviation can be unequal speed of the left and right robot wheels. Hence it is essential to detect such deviations and perform corrective operations to bring the robot back to the track. In this paper we propose a novel algorithm that uses image matching using SURF to detect deviation of a robot from the trajectory and subsequent restoration by corrective operations. This algorithm is executed in parallel to positioning and navigation algorithms by distributing tasks among different CPU cores using Open Multi-Processing (OpenMP) API.
Highlights
Terrain irregularities have been shown to affect wheeled mobile robot navigation [1]
Ground irregularities, slipping of robot wheels or unequal speed of left and right wheels may cause the robot to stray from the trajectory
In this paper we enhance a previous work, which is vision-based robotic navigation using Speeded Up Robust Feature (SURF) [2], by adding a deviation correction algorithm that can restore a robot back to the track
Summary
Terrain irregularities have been shown to affect wheeled mobile robot navigation [1]. In this paper we enhance a previous work, which is vision-based robotic navigation using Speeded Up Robust Feature (SURF) [2], by adding a deviation correction algorithm that can restore a robot back to the track. This algorithm is dynamic since it is active throughout the trajectory and auto-corrective because it is initiated automatically, when a deviation occurs.
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