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JACIII Vol.20 No.1 pp. 49-56
doi: 10.20965/jaciii.2016.p0049
(2016)

Paper:

Vision-Based Mowing Boundary Detection Algorithm for an Autonomous Lawn Mower

Tomoya Fukukawa*, Kosuke Sekiyama**, Yasuhisa Hasegawa**, and Toshio Fukuda***

*Department of Mechanical Science and Engineering, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
**Department of Micro-Nano Systems Engineering, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
***Faculty of Science and Engineering, Meijo University
1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan

Received:
April 1, 2015
Accepted:
November 5, 2015
Online released:
January 19, 2016
Published:
January 20, 2016
Keywords:
autonomous lawn mower, texture classification, a bank of filters, Gabor filter, RANSAC
Abstract

This study proposes a vision-based mowing boundary detection algorithm for an autonomous lawn mower. An autonomous lawn mower requires high moving accuracy for efficient mowing. This problem is solved by using a vision system to detect the boundary of two regions, i.e., before and after the lawn mowing process. The mowing boundary cannot be detected directly because it is ambiguous. Therefore, we utilize a texture classification method with a bank of filters for classifying the input image of the lawn field into two regions as mentioned above. The classification is performed by threshold processing based on a chi-squared statistic. Then, the boundary line is detected from the classified regions by using Random sample consensus (RANSAC). Finally, we apply the proposed method to 12 images of the lawn field and verified that the proposed method can detect a mowing boundary line with centimeter accuracy in a dense lawn field.

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Last updated on Mar. 28, 2017