Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 430966
Title Image-Based Particle Filtering For Robot Navigation In A Maize Field
Author(s) Hiremath, S.; Evert, F.K. van; Heijden, G.W.A.M. van der; Braak, C.J.F. ter; Stein, A.
Event Workshop on Agricultural Robotics: Enabling Safe, Efficient, Affordable Robotics for Food Production, 'Designing Sustainable Energy, 2012-10-11
Department(s) Biometris (WU MAT)
PPO/PRI AGRO Duurzame Bedrijfssystemen
Biometris (PPO/PRI)
Publication type Contribution in proceedings
Publication year 2012
Abstract Autonomous navigation of a robot in an agricultural field is a challenge as the robot is in an environment with many sources of noise. This includes noise due to uneven terrain, varying shapes, sizes and colors of the plants, imprecise sensor measurements and effects due to wheel-slippage. The drawback of current navigation systems in use in agriculture is the lack of robustness against such noise. In this study we present a robust vision-based navigation method based on probabilistic methods. The focus is on navigation through a corn field. Here the robot has to navigate along the rows of the crops, detect the end of the rows, navigate in the headland and return in another row. A Particle Filter based navigation method is used based on a novel measurement model. This model results in an image from the particle state vector that allows the user to compare the observed image with the actual field conditions. In this way the noise is incorporated into the posterior distribution of the particle filter. The study shows that the new method accurately estimates the robot-environment state by means of a field experiment in which the robot navigates through the field using the particle filter.
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