Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers

Mathias Franzius, Mark Dunn, Nils Einecke, Roman Dirnberger; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 44-52

Abstract


Currently, the only mass-market service robots are floor cleaners and lawn mowers. Although available for more than 20 years, they mostly lack intelligent functions from modern robot research. In particular, the obstacle detection and avoidance is typically a simple physical collision detection. In this work, we discuss a prototype autonomous lawn mower with camera-based non-contact obstacle avoidance. We devised a low-cost compact module consisting of color cameras and an ARM-based processing board, which can be added to an autonomous lawn mower with minimal effort. For testing our system, we conducted a field test with 20 prototype units distributed in eight European countries with a total mowing time of 3,494 hours. The results show that our proposed system is able to work without expert interaction for a full season and strongly reduces collision events while still keeping the good mowing performance.

Related Material


[pdf]
[bibtex]
@InProceedings{Franzius_2017_CVPR_Workshops,
author = {Franzius, Mathias and Dunn, Mark and Einecke, Nils and Dirnberger, Roman},
title = {Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}