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Occupancy Grid Learning Using Contextual Forward Modelling

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Abstract

A mode versus clarity dilemma exists in occupancy grid based robotic mapping. This arises as two general approaches have emerged in the domain with diametric operational modes and differing representational abilities; the inverse and the forward approach. Their classification relates to the sensory model employed by the approaches. The inverse approach is characterised by an ability to construct a map in real time. This ability comes at the cost of reduced representational clarity however. The forward approach is capable of producing more accurate maps but requires all sensory data a priori. This work investigates if sub dividing the mapping problem into its constituent elements of sensor data evaluation and representation may facilitate improved real time map generation. ConForM (Contextual Forward Modelling) is presented as a technique for spatial perception and map building which addresses this problem which embodies this approach. Results from in-depth empirical evaluation illustrate the associated improvement in map quality resultant from the technique.

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  1. School of Computing and Technology, University of West London, London, UK

    Thomas Collins

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Collins, T. Occupancy Grid Learning Using Contextual Forward Modelling. J Intell Robot Syst 64, 505–542 (2011). https://doi.org/10.1007/s10846-011-9553-9

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  • DOI: https://doi.org/10.1007/s10846-011-9553-9

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