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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 545593
Title Robot navigation in orchards with localization based on Particle filter and Kalman filter
Author(s) Blok, Pieter M.; Boheemen, Koen van; Evert, Frits K. van; IJsselmuiden, Joris; Kim, Gook Hwan
Source Computers and Electronics in Agriculture 157 (2019). - ISSN 0168-1699 - p. 261 - 269.
DOI https://doi.org/10.1016/j.compag.2018.12.046
Department(s) PPO/PRI AGRO Field Technology Innovations
PPO/PRI AGRO Toegepaste Plantenecologie
PE&RC
Farm Technology Group
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Autonomous robot navigation - Kalman filter - Orchard - Particle filter - Probabilistic localization
Abstract

Fruit production in orchards currently relies on high labor inputs. Concerns arising from the increasing labor cost and shortage of labor can be mitigated by the availability of an autonomous orchard robot. A core feature for every mobile orchard robot is autonomous navigation, which depends on sensor-based robot localization in the orchard environment. This research validated the applicability of two probabilistic localization algorithms that used a 2D LIDAR scanner for in-row robot navigation in orchards. The first localization algorithm was a Particle filter (PF) with a laser beam model, and the second was a Kalman filter (KF) with a line-detection algorithm. We evaluated the performance of the two algorithms when autonomously navigating a robot in a commercial Dutch apple orchard. Two experiments were executed to assess the navigation performance of the two algorithms under comparable conditions. The first experiment assessed the navigation accuracy, whereas the second experiment tested the algorithms’ robustness. In the first experiment, when the robot was driven with 0.25 m/s the root mean square error (RMSE) of the lateral deviation was 0.055 m with the PF algorithm and 0.087 m with the KF algorithm. At 0.50 m/s, the RMSE was 0.062 m with the PF algorithm and 0.091 m with the KF algorithm. In addition, with the PF the lateral deviations were equally distributed to both sides of the optimal navigation line, whereas with the KF the robot tended to navigate to the left of the optimal line. The second experiment tested the algorithms’ robustness to cope with missing trees in six different tree row patterns. The PF had a lower RMSE of the lateral deviation in five tree patterns. In three out of the six patterns, navigation with the KF led to lateral deviations that were biased to the left of the optimal line. The angular deviations of the PF and the KF were in the same range in both experiments. From the results, we conclude that a PF with laser beam model is to be preferred over a line-based KF for the in-row navigation of an autonomous orchard robot.

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