Abstract
We propose a new global localisation approach to determine a coarse position of a mobile robot in structured indoor space using colour-based image retrieval techniques. We use an original method of colour quantisation based on the baker's transformation to extract a two-dimensional colour pallet combining as well space and vicinity-related information as colourimetric aspect of the original image. We conceive several retrieving approaches bringing to a specific similarity measure integrating the space organisation of colours in the pallet. The baker's transformation provides a quantisation of the image into a space where colours that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image. Whereas the distance provides for partial invariance to translation, sight point small changes, and scale factor. In addition to this study, we developed a hierarchical search module based on the logic classification of images following rooms. This hierarchical module reduces the searching indoor space and ensures an improvement of our system performances. Results are then compared with those brought by colour histograms provided with several similarity measures. In this paper, we focus on colour-based features to describe indoor images. A finalised system must obviously integrate other type of signature like shape and texture.
Highlights
The autonomous robot navigation in a structured interior or unstructured external environment requires the integration of much functionality, which goes from the navigation control to the mission supervision, while passing by the perceived environment modeling and the planning of trajectories and strategies of motion [1]
The hierarchical procedure being consuming in computational time, the computing time of the global solution tends to increase to reach 4 seconds, a time considered to be acceptable for the task of global localisation
In order to validate this work, we compare these results with a classical image retrieval technique which uses colour histogram
Summary
The autonomous robot navigation in a structured interior or unstructured external environment requires the integration of much functionality, which goes from the navigation control to the mission supervision, while passing by the perceived environment modeling and the planning of trajectories and strategies of motion [1]. Among these various functionalities, the robot localisation, that is, the capacity to estimate constantly its position is very significant. Proprioceptive sensors measure displacements of the robot between two moments The integration of their measures allows estimating the current position of the robot compared to its starting one. The exteroceptive sensors measure the absolute position of the robot by observing benchmarks whose positions are known in an environment frame-attached reference
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call